{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# EnsembleVoteClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementation of a majority voting `EnsembleVoteClassifier` for classification."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> from mlxtend.classifier import EnsembleVoteClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `EnsembleVoteClassifier` is a meta-classifier for combining similar or conceptually different machine learning classifiers for classification via majority or plurality voting. (For simplicity, we will refer to both majority and plurality voting as majority voting.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "![](./EnsembleVoteClassifier_files/voting.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `EnsembleVoteClassifier` implements \"hard\" and \"soft\" voting. In hard voting, we predict the final class label as the class label that has been predicted most frequently by the classification models. In soft voting, we predict the class labels by averaging the class-probabilities (only recommended if the classifiers are well-calibrated).\n",
    "\n",
    "![](./EnsembleVoteClassifier_files/majority_voting.png)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**\n",
    "\n",
    "If you are interested in using the <code>EnsembleVoteClassifier</code>, please note that it is now also available through scikit learn (>0.17) as <a href=\"http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingClassifier.html\"><code>VotingClassifier</code></a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Majority Voting / Hard Voting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hard voting is the simplest case of majority voting. Here, we predict the class label $\\hat{y}$ via majority (plurality) voting of each classifier $C_j$:\n",
    "\n",
    "$$\\hat{y}=mode\\{C_1(\\mathbf{x}), C_2(\\mathbf{x}), ..., C_m(\\mathbf{x})\\}$$\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Assuming that we combine three classifiers that classify a training sample as follows:\n",
    "\n",
    "- classifier 1 -> class 0\n",
    "- classifier 2 -> class 0\n",
    "- classifier 3 -> class 1\n",
    "\n",
    "$$\\hat{y}=mode\\{0, 0, 1\\} = 0$$\n",
    "\n",
    "Via majority vote, we would we would classify the sample as \"class 0.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weighted Majority Vote"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to the simple majority vote (hard voting) as described in the previous section, we can compute a weighted majority vote by associating a weight $w_j$ with classifier $C_j$:\n",
    "\n",
    "$$\\hat{y} = \\arg \\max_i \\sum^{m}_{j=1} w_j \\chi_A \\big(C_j(\\mathbf{x})=i\\big),$$\n",
    "\n",
    "where $\\chi_A$ is the characteristic function $[C_j(\\mathbf{x}) = i \\; \\in A]$, and $A$ is the set of unique class labels. \n",
    "\n",
    "Continuing with the example from the previous section\n",
    "\n",
    "- classifier 1 -> class 0\n",
    "- classifier 2 -> class 0\n",
    "- classifier 3 -> class 1\n",
    "\n",
    "assigning the weights \\{0.2, 0.2, 0.6\\} would yield a prediction $\\hat{y} = 1$:\n",
    "\n",
    "$$\\arg \\max_i [0.2 \\times i_0 + 0.2 \\times i_0 + 0.6 \\times i_1] = 1$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Soft Voting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In soft voting, we predict the class labels based on the predicted probabilities $p$ for classifier -- this approach is only recommended if the classifiers are well-calibrated.\n",
    "\n",
    "$$\\hat{y} = \\arg \\max_i \\sum^{m}_{j=1} w_j p_{ij},$$\n",
    "\n",
    "where $w_j$ is the weight that can be assigned to the $j$th classifier.\n",
    "\n",
    "Assuming the example in the previous section was a binary classification task with class labels $i \\in \\{0, 1\\}$, our ensemble could make the following prediction:\n",
    "\n",
    "- $C_1(\\mathbf{x}) \\rightarrow [0.9, 0.1]$\n",
    "- $C_2(\\mathbf{x}) \\rightarrow [0.8, 0.2]$\n",
    "- $C_3(\\mathbf{x}) \\rightarrow [0.4, 0.6]$\n",
    "\n",
    "Using uniform weights, we compute the average probabilities:\n",
    "\n",
    "$$p(i_0 \\mid \\mathbf{x}) = \\frac{0.9 + 0.8 + 0.4}{3} = 0.7 \\\\\\\\\n",
    "p(i_1 \\mid \\mathbf{x}) = \\frac{0.1 + 0.2 + 0.6}{3} = 0.3$$\n",
    "\n",
    "$$\\hat{y} = \\arg \\max_i \\big[p(i_0 \\mid \\mathbf{x}), p(i_1 \\mid \\mathbf{x}) \\big] = 0$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, assigning the weights \\{0.1, 0.1, 0.8\\} would yield a prediction $\\hat{y} = 1$:\n",
    "    \n",
    "$$p(i_0 \\mid \\mathbf{x}) = {0.1 \\times 0.9 + 0.1 \\times 0.8 + 0.8 \\times  0.4} = 0.49 \\\\\\\\\n",
    "p(i_1 \\mid \\mathbf{x}) = {0.1 \\times 0.1 + 0.2 \\times 0.1 + 0.8 \\times 0.6} = 0.51$$\n",
    "\n",
    "$$\\hat{y} = \\arg \\max_i \\big[p(i_0 \\mid \\mathbf{x}), p(i_1 \\mid \\mathbf{x}) \\big] = 1$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [1] S. Raschka. [Python Machine Learning](https://github.com/rasbt/python-machine-learning-book). Packt Publishing Ltd., 2015."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1 -  Classifying Iris Flowers Using Different Classification Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X, y = iris.data[:, 1:3], iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5-fold cross validation:\n",
      "\n",
      "Accuracy: 0.90 (+/- 0.05) [Logistic Regression]\n",
      "Accuracy: 0.93 (+/- 0.05) [Random Forest]\n",
      "Accuracy: 0.91 (+/- 0.04) [Naive Bayes]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import numpy as np\n",
    "\n",
    "clf1 = LogisticRegression(random_state=1)\n",
    "clf2 = RandomForestClassifier(random_state=1)\n",
    "clf3 = GaussianNB()\n",
    "\n",
    "print('5-fold cross validation:\\n')\n",
    "\n",
    "labels = ['Logistic Regression', 'Random Forest', 'Naive Bayes']\n",
    "\n",
    "for clf, label in zip([clf1, clf2, clf3], labels):\n",
    "\n",
    "    scores = model_selection.cross_val_score(clf, X, y, \n",
    "                                              cv=5, \n",
    "                                              scoring='accuracy')\n",
    "    print(\"Accuracy: %0.2f (+/- %0.2f) [%s]\"\n",
    "          % (scores.mean(), scores.std(), label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.90 (+/- 0.05) [Logistic Regression]\n",
      "Accuracy: 0.93 (+/- 0.05) [Random Forest]\n",
      "Accuracy: 0.91 (+/- 0.04) [Naive Bayes]\n",
      "Accuracy: 0.95 (+/- 0.05) [Ensemble]\n"
     ]
    }
   ],
   "source": [
    "from mlxtend.classifier import EnsembleVoteClassifier\n",
    "\n",
    "eclf = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], weights=[1,1,1])\n",
    "\n",
    "labels = ['Logistic Regression', 'Random Forest', 'Naive Bayes', 'Ensemble']\n",
    "for clf, label in zip([clf1, clf2, clf3, eclf], labels):\n",
    "\n",
    "    scores = model_selection.cross_val_score(clf, X, y, \n",
    "                                              cv=5, \n",
    "                                              scoring='accuracy')\n",
    "    print(\"Accuracy: %0.2f (+/- %0.2f) [%s]\" \n",
    "          % (scores.mean(), scores.std(), label))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting Decision Regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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1pVdpampyf7+1bCtby7YCcM28azoFgt3Vh/+cYc+hsO075FhGhKw/QojQk9jU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ixRbpJBlJtEsi/ZvDSUlpSVwPFA3wl6hnAMyZBELTEtLWvYsmUde+01gWW8GXWbdIqXdJq8x180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\nto8FjZfwq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VHbKitrtOqu5JSmhIk4l3KTSPFQkSKjnOkz34DffwyX6yb8/rvp75+S0Kq6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fc10860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mlxtend.plotting import plot_decision_regions\n",
    "import matplotlib.gridspec as gridspec\n",
    "import itertools\n",
    "\n",
    "gs = gridspec.GridSpec(2, 2)\n",
    "\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "\n",
    "labels = ['Logistic Regression', 'Random Forest', 'Naive Bayes', 'Ensemble']\n",
    "for clf, lab, grd in zip([clf1, clf2, clf3, eclf],\n",
    "                         labels,\n",
    "                         itertools.product([0, 1], repeat=2)):\n",
    "\n",
    "    clf.fit(X, y)\n",
    "    ax = plt.subplot(gs[grd[0], grd[1]])\n",
    "    fig = plot_decision_regions(X=X, y=y, clf=clf)\n",
    "    plt.title(lab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1pVdpampyf7+1bCtby7YCcM28azoFgt3Vh/+cYc+hsO075FhGhKw/QojQk9jU\nPYlLoZGRmcGbq14NWXuBxqa+JEmK6JdkXmvPXFVkVj2zCrRxp6rhqwYqnq2gpKRE7lZFwOa9m3tc\n75mUZNtH4goLDnt8+DolhAgpiU3dk7gUnXzFpkiSJEX0OzKv1T+xXkWmP/BOTIQQ/ZfEJt8kLolA\nSJIi+h2Z1+qfWK8iE408kxIXSU6EGBgkNvkmcUkEQpIU0e/IvNbAxGIVmWgjIyZCDHwSm/wncUn4\nQ5IUEdW6utsSyLzWgXC3ZiD8DLFERkuEGPhiPTb19/6L/kGSFBGVfFVI8TWvdSBUWBkIP0MskdES\nIQa+WI9N/b3/on8JOElRSuUDDwCzgBTga+AKrfW6EPdNxDBfFVJ8zWsdCBVWBsLPMNB5j5pIchI5\nEptEX4j12NTf+y/6l4CSFKXUIOBT4APgdKAeGAMcCH3XRKwKpEJKV/NaQ1lhxdeQdriGvKVKTHSS\nqVzRSWKT6AuxHpskLom+FuhIyq1Ahdb6Zx7LdoWwP0IEXSElFBVWfA1ph3vIW6rERBeZyhX1JDaJ\nsIv12CRxSfQ1U4DbzwbWKqX+rZSqVUqtU0r9zOdeQgTAs0KKJ38rpAS7Pxwe0s6/Np+JCyeSf20+\nqzasYtG9i/xaH6xQ/Ayi93ZXd/yA8wWLkqBEK4lNIuxiPTZJXBJ9LdCRlFHAdcCfgXuBacACpVSb\n1vq5UHdOxKZg30ob7P6+hrTLVpeFfchb3szb9+T5kn5NYpMIu1iPTRKXRF8LNEkxAau11nc4v9+g\nlJoAXAt0GQjef/l9Pnjlg07LTz3/VGZeMDPAw4tYEexbaV37f/HYF5Tby0mIS6DkJP/29xzSbtnT\ngqXWQlJukntIu7ysvE+GvOXNvOElz5cMKBKbRJ+I9dgkcUn0pUCTlD3AFq9lW4Dzutth5gUz5YIv\nAhaqt9Jqh0a3a7TSfu+Tk5+DSZvYcM8GWmpb0GgUipQjUjA7zBQWF/bJC7vkzbzhIc+XDEgSm0Sf\niPXYJHFJ9KVAk5RPgSKvZUXIA4oiTHr7Vlp3mcSbAi+TmFuQS6IpkQO7D5B/VT7px6TTuLGR6sXV\n5CXmUTytuE+HvOXNvMGJhhGT999/leeeW8C2bZs4dKiBpKQkRo4s5I47HmPKlJP6tC8DlMQm0adi\nPTZJXOr/3n39XZ7+29Ns2bSFQ4cOGXFp1Ejuefgepp4wNdLdAwJPUh4GPlVK3Qb8G/gO8DNAxvli\nVDTeTfHGAduQAAAgAElEQVSctxufEU/NhzVkFGUw4rIRfs3Lra2sxaqtFFxaQEJhAu3N7SQXJlPw\n/wpo/ncztZW1MuQd5WpqoN1h/DkaRksWLryLXbvKmTLlBMaNm0RdXTXvvruUSy89hcWL/0dJSRju\n6K9ebXydPS30bUcfiU2iA4lNEptEzx7+/cN8+823TC2ZyviJ46ndU8s7b7zDRWdcxLOvP8sJ3zsh\n0l0MLEnRWq9VSp0L3A/cAewEbtZa/zMcnRPRK5rfOltXXYfFYqHsL2VYDlhQ8QrdrknKSiI5Ltmv\nMpF27Iw8aSTmTDM2qw1zghnbEBub/rPJvb8MeUeP1WuMrwf2Qv0+48/RkJy4XHvt7Xz/++eSmJjo\nXnbJJZ8yZ84M/vznW/nPf9aG5kCuxASYPa02NG32AxKbhIvEJolNwj83/PoGZs6e2SEurVu1jgtm\nXsADv32ApSuXRrB3hoDfOK+1fgt4Kwx9Ef1INL91Nic/h4O7D2IeZqZgfgFpE9No2tREzeIaDlYd\nDKhMZO70XMzxxn+TfV/t6zSvV4a8I8uVnAAUWWZQ11bBfvs2suzDItepLsyadVGnZZMnn8iQIdns\n2VMVmoM4E5RYSk48SWwSILHJRWKT8OVHF/yo07IpJVMYkj2E6qou5klHQMBJihDR/tbZ+j31kAC5\nl+eSdUoWmCDrlCy0TbP7T7up31PfY/88yyzabXZSC1NpLm9m9wu7O83rLVtdRnlZOYXFhRRPK+6L\nHy+meSYlYCQmfWHXrq/55JNlJCQkcvrpF5KVFZriCM3NTRxxRH5wjcTo6IkQ3iQ2SWyKNauWr2LT\nuk3kDs1l1nmziI+PD7rN5qZmcodGR4IrSYoIWLS/dba8rBxToon0iek4bA738vRj0jElmigvK/d5\n0b7kpkvYMGcDm363CeKBdhiWP4xLbroEgLrddcyfM5/d1bs7rH/wpQfJGSYvtAol79GSvuRwOPjj\nH2/kzQ//hc3cDsBji+/hmstu49JLbw6q7Uce+Q0tLS2cdlq3Bai655GYgCQnQoDEJpDYFCsOHjjI\ntRdfy9byrdjNdkwOEw8/8DAPPfpQUA+9/+nuP9HS0sKss2eFsLe9F+gb54WI+rfOFhYXQju0ftVK\nQmICCfEJJCQm0PpVK7Q71/vwwoIXaE1pZfwt45l0+yTG3zKe1pRWXljwAgDz58ynpq2GgvkFHL3k\naArmF1DTVsP8OfPD/ePFjNVrDicoRZYZfZ6gAPzzn4/x+scvMOTiwUx6/lgm/mMSSack8tiSe/jy\nyxW9bnf16o9ZtOhPDB06nFtuuS+QHTtM63J9hBASm0BiU6y47frb2Lp3KyN+PYLjXj6OsX8aS1Ne\nE7+4/he0tbX1qs1Vy1fxxMNPkD8sn1/e88sQ97h3ZCRFBCyQt84G+/CeryHrrtovnlbMsPxhVC82\n5lR6lmkclj+sQztd7d/VlAGAxKxE1j6xlhXLVrC7ejcF8wsY8oMhAO6vlQ9WUra6zH0MeXjRf5Ga\nytWTpW8+R8qUVAoudj6EnwaFN45hw6b1/PvfT3DccdMDbvPbb7dx3XVnkZiYyJIl72Ey+XmvqLQU\nMvMkKRGiGxKbJDbFgv31+1m9bjW5V+SSM91IvDPGZlA4t5CvbvqKN/79BhdcekFAbe7csZOrL7ya\nxMREnn/zef/jUphJkiJ6xVeZw2ArrPgasvbV/oMvPcj8OfOpfLCy0/6++udrysDm1Zsh3ggwntKP\nSYd4Y0j/yHFHRm2FmWgSyalc/jhwqJ6Uk5I7LFNxiqSjkqivDTxZqKur4eKLT6K93cpTT73DUUeN\n871TbS3s2wd5Mq1LCF8kNklsGuj27N6DXdtJHdXx7yt5ZDLEQ3VFYA+919XWccH3L8BqtfLcG88x\nauyoUHY3KJKkiF7x9dbZYCuseA5Ze95tmj9nPkuWL/HZfs6wHJYsX9Lt3a6e9p9zw5we39o7YdoE\nXnvxNRo3NrrvUgE0bmx0D9lHc4WZSPIeLYHoTE5chuUexdelm9GXapRJAdDe3E7LlhaOnDImoLZa\nWpr48Y+Pp6HhIA8//BJTp57c/ca7d7v/OCq3mWFDLMycXA/InHIheiKxSWLTQHdk4ZEkmZM4uO4g\nWZOz3MsbShtQVsX4Y8b73VZzUzNnTz+bhoMNLHxmIdNOiq73akmSIoLSVZnDYCuslK0u63HIesWy\nFX63XzytuNNQvK/+zblhTo9TBqafOZ1hD3Y/ZJ89NDuqK8xEQrSPmHTn0otv4jd/uJJt928l74d5\n2C129rxSTWJzUkAPzttsNi688Hhqaqq5885HOO20bobiPZKTscOaAajeG9SPIERMktgksWmgSk1L\nZdYZs3j19VdRcYqsaVm0fttK9T+rGZk3klN/dKpf7djsNs6Zfg411TXc8+d7mHVudDws70mSFBFy\nwVZYKS8r73HIevPqzX6339XdKs/+2dpt7hdiee7va8pAT0P2tVW1UV1hJtz622hJT77//bP5xb77\nWPTMA5R/Xo7SirzBw5h/15858sgiv9u55poz2LFjK2PGjKeubg8LFtzeYf1N518PHE5MhBChJ7Ep\ntmPTQHL7Q7djd9h5+7W3qX+5HpM2MXH8RO5/7H6/nye54qwr+Hrb14wdN5a9NXv5y+/+0mH9vLvm\nhaPrAZEkRYScZ4WVroakfVVYcVVA6W7IesK0CXz6yac9tt/jvOH8HOKIY9fKXSSMT0CjUSisX1mJ\n03Hk5Of4nDLQ05C9w+EI6ufvr/rraIkvF154DeeeeyWbN68hMTGJoqJJAT9UuGvX1wB8/fUWvv56\nS6f1N51/vSQoQoSZxKbYjE0DkTnOzD1/vYd5B+bx9Vdfk5efR8FRBQG1sbN8JwDbt25n+9btndZL\nkiIGpEAqrHTFVwWU6WdO58sVX/bY/hUzruhx3nCCSqDyuUryr8o/vP75avIS8zr0z9dbe7sasg/2\n5+8vBtKIiS9ms5nJk7/b6/0/+GBXxwWdpnVJgiJEuElsio3YFEsGZQ3i+BOP79W+K7euDHFvQk+S\nFBEWvoakffFVAaWn9v2ZN9zmaCN9WDo1i2vY49iDMinS89Npq2sLybxcV/9WLVyF1WolISGBkhn+\n//zRKhrLBPcbXTxvIoToWxKbBmZsEgOTJCkiLHwNSfviqwJKT+37O284ISuBpqomHDYHpngTCYMT\nsO61hnRerjIpVLxyV4bqb2JptCSsnAmKJCdCRJbEJkN/j00iNkiSIsLK15C0L10NWftq3595w8v+\nuYyW8haG3zSc1HGpNG9tpvof1eg6HZJ5ue4yjzf0zzKPA/X5kj4noydCRCWJTf0zNonYIkmKiKhw\nvPXW17zhsZPGYkowkTU7i7Rj0ohLjiPtmDQGnzmYA08fCLp/wZa5jBSZyhUiHokJSHIiRH8ksUmI\nyJMkRUREsG/99cVXGcaUQSkMKR6Ctc7qrqAyZMIQ2ga1UVddR1pmWq/7F2yZy74iU7lCTEZNhOj3\nJDZFPjYJ4SJJioiIcL/11lcZxvi4eOIPxJNXkueuRb/v833Ex8WTk58TVP+CLXMZbjKVKwzkmRMh\nBgSJTUJED0lSRJ+L9JBzV2UY9325z12GEQiqf9FW5jGWRkyUMqE1OBz2vjlgH4yeOLQGwKTkAVch\nwklik4yihIPJZEJrjcPhiHRXQsZms+HQjoDfGRYoSVJEn+uLIeceX5g1LKfHMpE7t+wMun/BlrkM\nVqw+X5KWNpj2dgc1NRXk5QX2Yiu/9fG0rm937warlcFpaWE/lhCxTGKTCIf0rHRs2kbVt1Xk5edF\nujshUVNVg81uIz0r3ffGQZAkRfS5vhhynj9nfo8vzOqpTGQo+hdsmctAxdJoSU+SklLIzh7HihXL\nAcjLG4HJFBeaxmtr3X8clWskJ9V7Q9O0N7vDQXNrK9t27qRs82bGZmeTkpTke0chRK9JbBLhkJSS\nRO6oXFauWMmB/QcoHFtIUkoSZnP/+xXcZrNRU1XDihUryBudR1JKeONS/ztDot8L95Czrxdmla0u\nc88B7qpMZCj7F2yZS1/k+ZLOTjjhAj777GXeeecD4uNNBD1Lat8+AIYNsbgXfRxkkz5pjbbbMTsc\nnDJuHN8ZOzbcRxQi5klsEuFy8tkns2XtFtZ9to71G9ZjMpkwqfBOlQoHh3Zgs9vIG53HyWefHPbj\nSZIiIsI15PzpXz+ltaWV5JRkTjzlxE5Dzt29MKsnni/MatvThnWvlYQjEtwvzCovK3e31d3dJFf/\nvnjsC8rt5STEJVByUuSHxGN1GlcgTCYTJ530YyyWFpqa9qN1L+cBl5YaX/Ng5uT60HXQDyalyEhJ\nIS0pKexzfoUQh0lsEuFgMpkonlbM+KnjaWlqobWxtV8+o2IymUjPSg/7CIpLQEmKUuou4C6vxVu1\n1keHrksiFrQ0trDh8w0cqD8AZrC0WNjw+QZaGltIzUj1OW+3J4XFheg2zTf3fkP7vnb38vgh8eg2\nTWFxod9lJrVDo9s1WulwnQqfZLSkd5KSUkhKSgl8x9Wrja+Zecye5priJVVvopnEJhEqEptEOJlM\nJtIy0kjLkGcM/aG09v8fuDMQnA+cCrgmUdi01vu722f5weXyP0h0csWMK6hpqyH38lySx6fQuqWF\n2qdryUvMY8nyJe71+Vfld5i361rvy+yxs7EOsjL0qqGkTkyleVMzexbvIeFgAm9sf4O//vqvRhnH\nnx4u41jxTAUlk0qY+8Bcn+vDSZ4viQBXYuJ0ODkZAGbPHvBlwSQ2iVCR2CRE+M0YNMOvuNSb6V42\nrXVdL/YTERDuh+N6M+Ttmpc7bP4wMk7KBOJJOCIBZVLsfmg3bzz9ht/zdrtSW1lLWk4aCeckkDQy\nCXujnaSRSeRdmIf1v1bKVpf1WMbR1/pwlaGUEZMIcSYoAyoxiU0Sm/oRiU39JzYJESm9SVLGKKV2\nAxbgc+A2rXVlaLslghXut+YGM+TtmpebMj4FiEOpOLR2kHJ0CsRD6ael7nm7nrqat9tl36rr0CbN\n2B+MxYKFfbX7GZI3mKS8JDa9sYnysvIeyzj6Wh/Kt/JKYhJBHqMnkqAMCBKb+gGJTf0jNgkRDQJN\nUlYBlwPbgKHA3cBypdQErbW8ajmKhPutub7KKPaksLgQbdU0bWoi6xQjKCkVR9PGJrRVM/nEyaz4\naAWNGxvdd6kAGjc2Qruxf088yzTahjtw2ONpaWylrbINszJTWFzYYxlHX+uDKUMpU7migCQnA5HE\npn6iv8YmR5smY/hktHUFDRsbGXLq4djUsKERbQWLqbDLa7zLgfoc7FYztesbUEcZsenQwVYadrZh\nbzNjMRVit5rZW9pA9gmHY0/9+ga/1u+uz6Glh+MDTDu+5/WRsnoNsH17pLsh+siM//Pv956AkhSt\n9Tse325WSq0GdgE/BnxPxhR9ItxvzfUuo6i1DmjIe/zU8ZgdCdQ8XYt2xJFSlErLtmZqn63F7Ejg\nR5f9iFefepXqxdU47A5Sx6fSvKWZmqdrGJY/zOfQvatM48pFK0n+QRrJo/LYu6GG1veaOKnkJIqn\nFXco45g2No2m7U3uMo7e60NRhlJGTCJMEpMBTWJT/9CfY1OcLYHLvvcAX77xDjWLvwEbpI5LpXlr\nMzXP7GFo1lhOH3WdMY7XnQwoLfqE1UteJeWMZJJHDWV/6R5a3m5lWtF5nD7qOnYWlbHuuWXEO5JJ\nHZ1O845Gal6oYWrRWZ3WZ44bTMPW/e71JRkX9nj8bUnLe3lm+0ZRzTFyfRYdBFWCWGvdoJTaDozu\nbpv3X36fD175oNPyU88/lZkXzAzm8KIb4X5rrmcZRYfdga3dhjne7PeQd+WOSjIycqj9toKqP32L\nKcGEw+rA0QK5Q0dQuaOSuxfdzQ2zb6DigQpUokK3aVISUrj7jbv96uOcG+bwzoz32P/EQUyJ1Tja\nHCSqROY8MwfoWGby0KFDZGRkdCgzGexbeaVUcBSRZ05ijsSm6NSfY1Pm4BFUV29n/rx/cetvp1Px\n4LeoBIW2apJNmcz/w7/86uN5Z8/no7nPc+DJekwJu3FYHSTYUzjv5vkAXHbJH+EFWLNgKYda95OR\nPJjjjznbWO6xfsPj71GhdmLWiUwZd6Z7vRADSVBJilIqDSMIPNvdNjMvmCkX/D4W7rfmFhYXQrsx\n/SpjRgbaAXab3e/pWAWjCxh33Ejsu5oYcsYQ4lLjsDfb2ff2PsaNHEnB6AIW3LaA1FGpjPzhSOJS\n4rC32Kl/q57XnnrNrykBj9z2CAyBoWdMwJyRhu1QE/vf/poFv1nAvc/dS2pGKjfddxOVF9SyaVUd\nI4pzuOm+m9zvpOjNW3lltCTKyOhJzJLYFJ2CiU09TaNysZgK0VZo2NhI5nQjNtna7X5Px5p6XPex\naWjGSBpH1bD08QUkHpVA/hnjiUuLw95kZ9/b9bzw3u2ce73v2LRkyW8g107eaROIy0jDfqiJ/e9+\nzcNPX8Lld95LETO45qoFVN/9LQ17djAsfzTXXLXAHZtSUjK49upHqauroL6+iuzs4eTkjPB9cpz8\nOY896Wm6mD9tR+t0MxGdAn1PykPAGxjD6MOA3wHtwEuh75rorXC/Nbd4WjHD8odRvbgaW7uNlAmZ\ntGxuYO+ze/2ajlW3u47y8nJGXTvKPeQPUDuslvInytmydot7SkD66HSadzeTOiyVlOwUv6YE7KnY\nQ+naDWRfXkDqkfm072sheUQ+plQL658pZU/FHoaOGMq65evYte0QWTm/ZNfWhaxfsZ7jTj6u07n0\n/OpJni+JQgO5lLDolsSm/iHY2OTr+lo0agbLsv5OzT/KsbfbSJ0wiObNB6h7Zq/P6Vjbkpb3GJuq\nnygnXW2h8pu1FHrFpkHDUqh8Yi0j83zHpl07NpDjEZvij8wnLt3Ct8+Usq9mDwyCjRs/pLKyhkGD\nbqeq6hE2bfqISZNO7dCWKzEJJEEJNj75M12sp2NE+3QzEX0CHUkZDrwIDAHqgJVAidZ6X6g7JoIT\n7HQlX+5/4X4un3EllQ/txpS4xz2d6v7l9/vc19eQf3lZOdZ2K9XvV3NgwQHsdjtxcXFkHZ2F1Wb1\nOSWgbHUZ7Q4bh9bUUvfKt8ZbEzQkjkim3WGjbHUZucNzeXXRm9ht32XwEXOoq17Dq4uWcez0Yzu8\n4fvLT75k4W1PcuN917gTGBkxiUIyahLrJDb1E+GOTXfctowb5x5D1UO7MSXscU+nuuOvy3zuG+nY\nVPFVGSUlDt5880ns9hKysi5l375VLFv2JBMnntIhNm3Y8AGLFv2aq69+oFMCI8RAEeiD83PC1RER\nWr2ZrhSIyvJKUlOOJN18IzhaISkJh+1Rqr6p8nkcX0P+hcWFNNc301LeQu41uSSNTsayo5W9z+xF\n12mf09WKpxVjdpiw1VsZfsNw98ONe57ag9lhonhaMeuWr2N76V7Ss/4AQHrWFWxb//MOoykOh4PF\nDyyj+tskFj+4jPYkI4GRxCRKyKiJcJLY1H+EMzZtS1rO9vq1JCQVkGi6EXQrJCahHY+yet8rnHnE\nLT3uv9tZfau76lm+YlNX1bU8pzf5ik0jji7mza1/ZWv5FhIzbsGiGklLu4by8qs6jKY4HA6WLXuC\nujq6TGCEGCjkX/UAl1uQy4TvTAhpguJwOHh10Zu0WyeRnnE66YPOIT3jDGztk3h10TIcDofPPk0t\nmUrFMxXULq/FUm+hdnktFc9WMLVkKtlDszElmBh85mBSJ6ZiHpRE6sRUBv9oMKYE3/9kTcqEOcnM\nkLOGkHFsBgmDE8g4NoMhs4dgTjLyclf/4+Pzsdv2Ex8/rEP/V6+BFxeto+rrvWSmz6N2ewPWNXZJ\nUKLM7Gm17o8Qov8IdWzalrScqcc5+Gr5mygmkX3E6WTnnkP2EWdgUpP4dGnPsanIMoOSjAuZWnQW\nNc/V0LDyENSbaVh5iJrnaxh9dM+xSceZ3O24Pt58xabJk3D3PyMjH4d9P2ZzAe3tx7Bs2ZPu/m/c\n+CE7dnxDevrNlJeXs2nTRyE5h0JEm6AenBexqXJHJZVf78FsruNg/Wz38rg4qNhuo3JHJSPHjuyx\njZ6G/Hdu2UnyoGSSRyZjrbOjTG1oh4PkI5OxDLJ0GlL3fkalrrqOlEEpDCkegrXOikajUAyZMIS2\nQW2UrS7r0H+73YrdkQDAto023nqtkuzhBWx8bRXKdjLJmT/m0KGuh9wB6uoqApoXLIIg07qEEN3o\nKTbtrbRRXb2d4cPH9dhGd9WzvvvTH1FX3X1sSsywkLl7GGQcbuvA3lqg97HJYrGSYEojLg6qqqC6\nejv5+WPd08HS0iQ2iYFNkhQRsILRBfzm8Zuxtlo7rUtITqBgdIHPNnoa8s/Jz0FbNa3lVtKOLcJu\n08SZFU3l29DWjtO9unpmJCc/h/i4eOIPxJNXkofNasOcYGbf5/uIj4uneFoxv3l8GBvWW6n6ehuf\nLn2Tk8+byXdH/pjExGQKs49j42rjTlV8/Fz27CknM/Niystv7vQAo8wL7iOSnAghfOgpNu3YmUB+\n/lifbXRXPWtb0nJ27+0hNrVpsrOHu9vZsOEDFi++k/rd13DJNYHFJmurlW2l23jp0Tc554c/Z/To\n40hMTCY/f6x7FCXaYlOwVcNc3lidK9d44SZJigiYyWRi/JTxIWkrtyC303B/zrAc4mwJ7H15Pw7H\nARJHZtK86yD1r+5nkE4jZ5iRpDgcDl55chm1lUkdHnrvqoLMvi/3uSvIVNYOBYZy5AQH6/6zgtaG\nIewtrePoWSdhMplwOIwHF222ibS3J2K1HqS5OYn4+Ikd7ljJvOAwk8REhFGofqkSfcOfqbY9xaa2\nBDBZ/L8+5+SM6DAKUWSZwZbsj7E1H45NCR6xKcOazZAhRpLiig2HalP5dOkyCie5CrLkUjBqKtv/\nsYo2C6SPy6Rx6z6qnq9g7KjDsclhdvDxm/+ltWEIZWWrOOusm6MiNhVZZrB6TfcVunxWX/OxP9Om\nwerVvLE6dNPTRXSaPdv3NiBJiohClTsqiVMp2KubqH58vfuFWXG2ZOKGprink7kefk/NuJ5t6//W\n4aF3z+lk21t3Eq8SGX10CVNmGRVkiiwzKC1932Ne7+Eyj9XV26mq+gaHo43W1vMwmVKxWJqJi8ug\nqirRPWWg47zgrstEil6SFzCKMJPny0Sg0r/JI1Fn07S71RmbTGirg7j2NExZaZ1iQ0rK9dRu/xvW\nNXYmTfoeADddNJlnX/gNG/7+Hrv1bhJUCtPGncdlF/2RFIsxV6y09H1qtjdEZWwK9v+Nz/2nTQuq\nfTGwyG1fEXUKRhdw+xO3MHbSUZjtJeiGeZjt36Fo0lHc/sQtFIwucD+8b2mZSsP+6Vhaj+vw0H7Z\ntlROuGAu0394OTQmcens+7n1yleZFDeLIssM9x0pu72EjIxLsdu/434wMT9/LHPnPsbw4dmkph7H\n0KGPk5IyheHDc5g79zHy88f2uL8IwurVkqAIIaJSfv5Y5s17nMKjJmK2nog++AvM1hMoPOoY5s17\nvENsaGs7lsbGEiyWyR1ig2s62UVn3YWpMZWLzrqLa69+lJQUI0GR2CTEYZKkiKhjMploaWqhtqId\nk5qHdlyCyTSPmop2WptbMZlM7lEUW/u52Kwm2lrPZdMXe3npH+vd0zjGtJxE+UdbsTTksHLl0g4X\n6cN3mq4BcJZ5NKqkmEwmWlsb2bu3kaysu0lOnkFW1t3U1R3CYmnCZDL1uL8IkCsxWb1aqnUJIaKW\nZ2wwmebhcMxBqXns3ds5Ntjt59DeDjbbOezY0TE2OBwOVq78L4cOpUlsEqIHkqTEgNrK8P3CV1tZ\ny+YvNof0GK5RktbmYqxt2SgTWC05tDYX8+qiZdhsNvf6Nks2mr3Y2nOwtU5iw6urGNNyEkWWGe6L\ndVLSzztcpA/P6z0Gs3k4dq8yjzabrcP6traNPa733t/7jlVdXUXIzs2A4jVqIomJELElnNfGcMWm\nN998EotlHG1tWShlor19MBbLuA6x4fD6+g7rvUsIS2wSomfyTMoA11X1q1BoPtTMonsXsXbVWmwO\nG2aTmaklU7n69qtJzUgNqm1XGUmrpRyH/Qu0tqNUHFaLZtuGFF54fA3bN+7B0lKOw74caEWTgtWa\nRFVVVocyjS0tE7BYppKUtNL9AKFrXm9cHDQ0nOY+rqvMY2npu+719fUltLTUkZKSQ3x8Rqf1Xe3v\nWeZSqn91Q6Z0CRHTwnVt7C42TZl1NcQF17Yrdlit+9H6s8OxyaqoqspyxwardT8Ox0dAC0hsEqLX\nJEkZwLqrfhUKi+5dxKoNqxhx7Qgyx2fSsKXBqKZ1L8x9YG5QbReMLuDcq0/j6fuXgmkw1tZG4uIz\nMCfu47wf/R+nF/+c+B+l8eKLj9CmcmlvtxIfn0hS0n7OPPOKDmUa29puRet42trOZMeOB9i06SMm\nTjyFefP+Tltba6djJyYmc9RRxzJvXg6trc288MLvKS9PpaBgMBdffAfJyanu9d3t7ypzKdW/vMgb\n4oUQhPfa2F1sqt0Lk66cFVTb+fljOfPMK4zY0zYIq7WB+PhMkpIOcuaZVzB58mmceeZOXnzxEZTK\nkdgkRJAkSRnAXM9tZAy+kW3rF3aofuXJ+2WIvtRW1rJ21VpGXDuC3OnGfknTk0Ab1bS82wu0/dVr\n4P3XNtHWNgqbdR9xcbei7Y+i7EWUli7n9NN/TmnpJzgco7Db92Iy3YzdvgC7fZR7/ZtvPklzcyEO\nRy4Qj8ORR3NzofuCPGbM8e7jdfXCqzFjjqe09H327m1g0KB57N37CFrb3ft57t8dqf7lJKWEhRAe\n/L02Bvoywp5i045H1nZqrzcvO/SMPXFxt3WKPRKbhAgdSZ0HKNdzHXbbd8kcPAe7raRD9SuXLz/5\nkl//5E6+/ORLv9uuq67D5rCROT6zw/LMozOxaRt11XW9an/1GuNTX1XJgV0HsVs3oR3jgBIcjjFY\nrXBFI1wAACAASURBVOuprNxBaem7VFaWY7Wuw+EYC0zrtL6qqpy2ts+Ay4GfAJfT1vYZVVU7qK7e\n7j7mhg0f8LvfnceGDR90On/BVEiRCivIMydCiE78vTZ2d23uSU+xya5s1NdXBdV+dfV2n7FHYpMQ\noSNJygDlGkVJz7oCgPSsK9i2fi/rV6x3b+M9Hczfi1ROfg5mk5mGLQ0dljd81YBZmd1vhPfVvisp\ncX2KLDMosszgxOxLmD37SpKTc8nJuZm8vAJycm4mOfkIZs++ksmTT/O5fvLkE9C6DfgZMM/5tY1j\njz2x2yFvfyus+COmK6xIpS4hRDf8uTb2dG3uye76HOxWM3tLG2hpwf3Zu76BOG12vxG+t+3n54+V\n2CREH5IkZQByjaK0WycRH5+P3baf+Phh2NondUgWDk8Hu65TAtMT1xvdK56poHZ5LZZ6C7XLa6l4\ntoKpJVPdU7u6ar+rpMT18VRa+glKHU9qahEJCe2kpo5DqeMpLV3uc73D4eCzz5YBJwInA98FTkbr\nE/n00zc6VVgxhrz9r7DiK6AFu3+/Je84EUL0wN9rY3fX5p4UWWZQknEhU4vOoua5GhpWHoJ6Mw0r\nD1HzfA1Ti85yT53qTfsuEpuE6DvyTMoA5KqOZTbXcbB+tnt5XBxUbLdRuaOSgtEF7ulgg4+YQ131\nmoAervd8o3uFrsCszJSUlBjLOZwoWVq/S2b2HJob17D4wWVcduexjLd+r8e2A6lw0tBwGjZbK2Zz\nsnv9hx8uobGxEVgDXAqkA41AG42NFkpL32XKlDPcQ95ZWZeyb98qvyuseFZI6U3/fe3f78gzJ0II\nP/hzbXRVv+rq2uxPbLrskj/CC7Dh8feoUDsx60SmjDvTWE7H6U6Bti+xSYi+JUnKAFQwuoDfPH4z\n1lZrp3UJyQkUjC7wmA72B8A1Hezn3T5c7y01I5W5D8yltrKWuuo6cvJzOjwc/+KidWxevZfk9D+Q\npNNRaTdQu/0qrGvsMKnnto23+vqucNLW1sqKFS/x7rsvcNpplzB9+hwSE5MZOXISe/aU8847L5OQ\n8AvM5lHYbN9gtf6J00+/mMmTT/O4U/VbwDXkfZVfFVZcQ/K97b+v/fsFSUyEEAHy59rY07XZn4e7\nXW90r6uroL6+iuzs4R0ePg+mfYlNQvQtSVIGIJPJxPgp47td72s6WCClinMLct3JietN7w6Hg42v\nrYL240mNK+o0pOzrjpXJZPJZoWTMmOOx2Ww89NBPsduHsWrV21x99ULMZjMOh4Pq6m+Jj/8eWVkX\nOfeYxv79a6mu3gXQ45C3d4WVQPnT/35NpnQJIXrB17XR13SkQErl5uSM6FQZK9j2JTYJ0bckSYlB\n/kwHGzl2pM92XEmJpyLLDKqqtgY0pNybMpAAS5f+hYYGG0r9Hw0ND/D66w9z3nm/6jSk7T3kHsgL\nr4QHGT0RQoRRuKcjBdq+xCYhIktprcN6gOUHl4f3ACJgDoeDbaXbup0OVjS5qMe7SZ7JifcD7672\ny8u/7HZIubDwOHf7vX3rrc1m46qrxnDgwAkodTta/56srFUsXvw1JpPJffyuhtyPOupYdu5c71f/\nYp68gLH/mz1bRboL0eiNN5DYFGUCiR3hbl9ikxDhM3s2fsUlGUmJQb6mg3nqbrTEV/v+DCkH89bb\npUv/wsGDNuAnGHn2HBoaVrrvWPU05A7+vfAqpsmoiRCij4V7OpLEJiH6F0nJRZdcZYKh51LBwXA9\nIJiScn2PZSDr6io6fG+z2Xj99b+j9VQgG4gHsnE4prJ06d+w2WyA95C7jddffzhkfR/Q5AWMQogY\nJrFJiOggSYoAOr9YEQh5UuLJ9QBjW9uxNDaWYLFM9vutw6Wl73qVcbzQ+XUNjY2NlJa+6w4WDscM\n4Ps4HCd1CBKiC/KeEyFEjJPYJET0CCpJUUrdqpRyKKX+EqoOib7TXVISzuTExXWnym4/h/Z2sNnO\nYccO/9467Hqrb0pKAoMG/YLs7PsZNOgXpKQkuN/q2/WQu9yx6sSVmMgb4sUAIXFJBENikxDRo9dJ\nilLqeOAaYEPouiPCobbS+KWzu9GSwZVHBpWUeA95+1rvulNlsYyjrS0LkymB9vbBWCzjunzrsPeQ\nu8lk6lDGMT19GllZFxEf/z2qq3fhcDj8GnKPaV6jJpKYiIFA4lL/EmjsCHf7EpuEiC69SlKUUmnA\n88DPgIMh7ZEIqS8/+ZIbz7qTF578Eug8WtLVkHUgfO3f1XpXGUar9TO0/ilwEQ7HT7FaP6eqqpzq\n6u09DrkfLuO4kYaG09yfuLiNVFWVd/FW385D7jFLRk3EACVxqX/pTewId/sSm4SILr2t7vUY8IbW\n+kOl1B2h7JAInudLFZ99YBkHa5PY8Ooqflh0c4e0NJgKJv7s3936/PyxnHnmFbz0/9m77zi56nr/\n46/PbM2WbAohYVOABAIhCUUiBjREJHDFa0QUlYhcBQSxgvwUCyiCIsUCguWCIt14vQgXgoj0JFKM\noAlkIZQlkEaSTdsku9k6398fZ3aZnZ26087MvJ+PxzxgT/3MyeZ88jnf7/l+F15PVdW3qaycQlfX\n63R1Xc2HP3zmgFmHe3svo7sbAoGP8vrrP0hq1t1kZvUtORqtS4qf8lKBGGruyPbxlZtE/CXllhQz\nOw04HPhO5sORoYrWjatzWQ8bX22lof7CqCOU9N1s6+vPjzuCSSyJ9o+3fvnyxZi9l7q6k6msnEFd\n3UeB97J8+ZKETe7gDdM4Y8axgz4HHvhuysvL4za5l9Q48+rWJSVAeamwpJM7sn185SYR/0ipJcXM\nJgDXAfOcc93J7PPo3Y/y2J8HN7ce//HjmXfqvFROL1HEmlix72ba2zubkSPPYOvWZwc8MUq0PpHw\n/evqPsnOnckfP9lZd7u6tuHcPzBrIBhspasryLp1IxPOupvtWYt9TxMwSgkZSl4CWLx4IUuXLhy0\nfM6cBcyduyCDEUo45aYSzk2JROQuKWLzj0pqs1S7ex0JjAH+ZWZ9s0WWAcea2VeAKhcxhf28U+ep\nGMmgZCdXfOdJ0SUA1NWdS3Pz2bz44hMcdtjxCdcn0rd/RcUFvP12Mw0Nn6a5+fykjh/eJB5r1t0P\nf3h13Cb3eBobp8Ztck+0f0HTEMJSelLOSwBz56oYyQflphLNTUlS7pJwqRYpjwIzI5bdCrwMXBUt\nEUj6IguTRCNx9T0p6uk5lPLyCfT2bqO8fCLd3YfywAM3MX363LjrEz2xeuf4M+nurqKrawdtbdVU\nVMxM+vjxZt0NBoMDmtwBKitnsG3b31m+fAknnfTFuN8/27MW+5KKEyldyksFQrmpBHOTSBpSKlKc\nc23AS+HLzKwN2OqcezmTgZWyZFtLYknUpNzXZJ1sk3NLyxrGjJk06PjBYCd79nyMQKCWjo42ysqG\ns25dVdLH75t1NxD4Gq2tP+b++6/lYx/7pprEU6EX4qXEKS8VDuUmEUmFpfuQycweB5Y75y6Mtn7J\njiV6ipWkWO+XpCoYDNLc/HzMJuX99z+C1av/HXP9lClH9j+tWrHiMX77229xzjlX9ze1B4NBXnvt\nn/z+999m7dqRNDRcQGvrtUyc2MpZZ13JlClHJjx+MBjk7LMPZMeO91FZeQddXZ9hxIinuPnm1wgE\nAnHjD4+vJKkwkVTMn2+JNyouifISwKJFKDflmHKTxBUaHl9KQJJ5aahDEPdzzn0g3WOUqlS7cSUr\nmSblZJqcYw3TGAgE2LNnF5s372LkyF9QVXUogUADLS1n09Gxm/Ly8oTHv/fen9La2kNZ2VcAKCv7\nCq2ti/ufWKlJPAoVJyJJUV7yJ+UmEUmFSv4cCx8qOHJiRb95Z1bd0wcM05ioX3HfrLyx9PT0hGbd\nfQ9m+xMMbsZsMs69R7PuxqJhhEVEAOUmkVKRdkuKxJfu+yX58s548OPp7r6LysrxMYdp7JNsv9zl\nyx/un3W3p2fgU6ldu9pYvvxhZs36ULa+WuFQy4mIyADKTSKlQ0VKlmTq/ZJ8eeGFx3nttWZ6e8eE\nZtXt5PXX1yU1q26iYRQPP/xEvvzln9HevnPQupqa4Zp1V8WJiEhUyk0ipUNFSoZk6/2SfOh7UtXZ\nOYbu7s0EAl+ju/t6Ojr2HjBM41CVl5dz/PGfzVi8kSO8FCRNwCgiEpdyk0hpUZGShkJvLYllw4ZX\nWbu2ma6utQSDHyAQOIpg8EC6up5k7doJvhpmMdoILwVHc5yIiCSk3CRSWvTi/BD0vfgO+Pal93Q0\nNk5l/vyzGDZsLGPGnM+4cRMZM+Z8hg3bm/nzz/LNrLiRI7wkeinSd5YtU4EiIpIk5SaR0qIiJQnh\nI3JFjspVrLxZdd9Nbe1BVFZ2U1t7MGbvZvnyJfkOrV/fCC/19ecPGOHF9yKKExUoIiLJUW4SKR3q\n7hWDn7py5bpfa6Zn1c1G/H19k3t7ZzNy5Bls3frsgPHyfUcvw4tIkVFuGqzgcpOIj6lICfHrUMH5\n6Nfa2Dg1rRFSwmUr/neeVF0CQF3duTQ3n82LLz7hv/6/6tIlIkVGuSm6gspNfhIxeIwIlHiR4vcR\nuWLNqpttycwKnIxsxZ9owi7fPLFS64mIFCHlptjHLYjclC1pFhrKkxKppIoUv7aWxDKwX+svCu5J\nTLbiz3STf0apMBGRIqfcFJ2vc1OOKO9JJpVEkeKn90uSVej9WrMZfyab/DNGxYmIlADlpth8mZtE\nClhRFimF1mISTaH3a81m/Jlq8s8YvXMiIiVCuSk23+UmkQLn/8ceSQofIhgGDhNcaAVKon6tmR5z\nvaVlTVrrI+U6/rzoG0ZYBYqIlAjlJhHJpYJtSSmG1pJYctmvNdEIJ0MZAaWo++WqW5eIlCjlJhHJ\npYIrUgrx/ZJU5apfa6IRToY6AkrR9cuNGLFExYmIlCLlpiKnYYDFZ3xfpBRzi0ksuerXmmiEk6GO\ngFJU/XLVnUtEBFBuKgXKdeInvnwnJfz9ksh3S4q9QMmV8BFOhg8/g97e9wzok5tofdHT+yYiIjmn\n3CQifXxVpER78V2y450nUecCfSOcNPPii08ktb5oRRQnKlBERHJHuUlE+uS1u1cpduXyg0QjnEyf\nPre0Zs3Vy/AiInmn3JRHeh9FfCjnRUopvPjud4lGOFm+/OHSGQFFXbpERHxBuSnLEhQiyoPiN+ac\nS35js/OALwL7hRY1AZc75x6Ktc9PH1ky6AQqTvIrGAzS3Px8zBFO9t//CFav/nfM9VOmHFn4T6vU\neiKlYv58y3cI2TaU3LRoEcknP8kJ5aYsW7ZM+U78Icm8lGpLylrgW8BrgAGfA+4zs8Odcy9H20EF\nif8kM8JJUY6AoqGERYpVyrlJ/Kdkc5OIRJVSkeKc+0vEokvM7IvAbECJoES1tKxhzJhJ+Q4jNrWa\niBQ15SaJxve5SUTiGnK7qJkFzOw0oAZ4JnMhSSFZseIxLrvsY6xY8Vi+Q4lOI3WJlBTlJoECyE0i\nklDKRYqZzTCzXUAn8GvgFOfcqoxHJr4XOeuvr8ap1zwnIiVFuUn6+Do3iUjShjK61yrgMKABOBW4\n3cyOVTIoPUOd9Tdr1K1LpJQpNwngw9wkIkOScpHinOsB3gj9+G8zOwo4H29klUEWL17I0qULBy2f\nM2cBc+cuSPX04hPhs/6OHHkGW7c+m79x6lWciJQ85SYBn+UmEUlLJuZJCQBVsVbOnasbfjF650nV\nJUDfrL9n5+6JlQoTEYlPuakE5T035ZMmZJQik1KRYmY/Bv4KrAHqgdOBucCJ8faT4pJoVuCsP7HS\nuyYiEka5ScAHuckHlBelmKTakrI3cBuwD9AKvACc6Jx7PNOBiX8lmhU4a7P+qvVERKJTbpL85SYR\nyYpU50n5fLYCkcLR2DiVCy/8TcxZfxsbp2buZJqAUUQSUG4SyHFuEpGsy8Q7KVJikpkVOG1qNRER\nkRTkJDeJSM6oSBH/0TsnIiIiIiVNRYr4h1pPRERERAQVKZJvKkxEREREJIKKFMkPFSciIiIiEoOK\nFMk9vXMiIiIiInGoSJHcUMuJiIiIiCRJRYpkl4oTERGRQfN+iUh8KlIk81SYiIiIvGPZMuVDkRSp\nSJHM0vsmIiIiIpImFSmSGWo9ERGRUqbuXCIZpSJFhi7ihqziRERESpJ6EYhknIoUSZ1aTUSk0Omp\nt2SY8qFIZqlIkdToaZGIFAHdw0RE/E1FiiRHrSciIiIikiMqUiQ2FSYiIiIikgcqUmQwFSciIiIi\nkkcqUmQgvXMiIiIiInmmIkU0lLCIiIiI+IqKlFKmbl0iIiIi4kMqUkqNWk1ERERExOdUpJQSvW8i\nIiIiIgUgkMrGZvYdM1tmZjvNbJOZ3WtmU7MVnGTIsmUqUESkaCk3iYgUn1RbUuYANwDPhfa9EnjY\nzKY55/ZkOjhJk945EZHSoNwkIlJkUipSnHMfCv/ZzD4HbAaOBP6eubBkyFSYiEiJUW4SESk+6b6T\nMgJwwLYMxCLpUpcuERFQbhIRKXhDLlLMzIDrgL87517KXEiSMrWeiIgAyk0iIsUinZaUXwOHAO+N\nt9HixQtZunThoOVz5ixg7twFaZy+xGkoYV9b09JCe2fnoOU1VVVMGjOmZGIQyYOkctPCxYtZuHTp\noOUL5sxhwdy5WQpNJL/ynRfyfX4pLOacS30ns18C84E5zrk18bZdtIjUTyCxqdXE99a0tPCxSy+F\nKDdiqqq457LLsn4z9kMM4hPz51u+Q8iVVHITixYpN0lJyXdeyPf5xUeSzEspt6SEksDJwNyESUAy\nS++cFIT2zk7o7OSH5eXsX1HRv3x1dzff6+yM+hSpGGMQySXlJpH48p0X8n1+KTwpFSlm9mtgAfAR\noM3MxoZWtTrnOjIdnISo9aQg7V9RwcGVlQMX9vSUXAwi2abcJJK8fOeFfJ9fCkeqLSnn4Y2Y8mTE\n8jOB2zMRkISoMBERSZZyk4hIkUl1npSUZqiXIVBxIiKSEuUmEZHik+48KZIJKkwKTrwRSgB6gkFW\n7dlDR3d3/7o3e3roCQZzFmOiGDTKiohIcfF7blJeklSoSMk3vQxfcBKNUHLxGWewtrWVL0e56XcE\nAmzfvTvrMW7fvTtuDC+vXcsVd9yhUVZERIqE33OT8pKkSkVKvqj1pGAlGqGkqqKC8cOH85WyMiaU\nv/NXbF1PD7/s7WVkXV3WYxxZVxc3hqqKCo2yIiJSRPyem5SXJFUqUnJJhUnOJGoyTmd9n3gjlPQE\ng4wpK6MxbFVnaHky8WXKhPJyJofd7AHo7e3/X42yIiKSO8pNykuSPBUpuaDiJKcSNXnf8LWv8dXr\nrx/y+mvOOy/u+V9eu5b1O3ZwCQP/gvUA64FHli/ntgcfzGqT9oZt21jf2sp3gAp7Z86kbudYD2xu\nbU3r+CIikppSz03KS5IqFSnZpndOci5Rk/f23bvTWt/R1RX3/Ls7OhgG/Ag4KGz5K8AXgW27dmW9\nSbujq4tq57gsEODgsGSwCvhyMEhn2EuLIiKSfaWem5SXJFUqUrIhrOUEVKDkS6Im43TXr464oUb+\n3AjsH3Yj3uVcSvFlwnhg3zgxJPoOIiKSWaWem5SXJFkqUjJJ3boKxrY9e3ios5MXy8r6l63v7WVb\nEsMwVldWQlUV3+vsHHzjrqqitrqaTuBB4F9hN9+NeH1/K8vL6QkG6ejuZk/Yrh3d3SkNA3nvM89E\nbR7fu6GBhtpaHLAxGKQmPAbncMDwmpq43yG8f3Ms2e67rKEoRaTUFHpuKva8lKtziEdFSiaoOCko\nDz7/PC27d3NNlHW7QuvjaRw1insuuyzmTeqmhx6iB/gtYGHrHF7f36Y1a2jZsYN1QGXY06R1ztGC\n12/34AkT4sZw7zPP8Nkrr6Qmyrp24P99+tOYGUQ8oQIwM8aOGBH3OyS60SbqW51u3+VsH19ExG8K\nPTcVe17K1TnkHSpS0qV3TnwrVpPx9t27qQWuAw4MW/8a8LXQ+nj7A3FvQjva2+Mef0d7OzhHhxld\nYYmgwzlveYJ+xeC9YFgDXB/jHC2treAcnWZ0B96ZjLszGOw/Rzo30kR9q9Ptu5zt44uI5Eux5qZi\nz0u5Ooe8Q0XKUKjlJO/iNbfWVFWxMxjkq7t2DVofqKpiWOjGsh9wcNi6vtv8sIoKusrK+GZHB3R0\nDNq/pqoq/vlDfXn3ZeDLiX2399qqKvYAlztHeVgTeo9z7CHUZJ/gO/Y5EDgiLJn0PaGqKCujw4wf\nAhVhT626gQ6z/nOkK17f5Uw0iWsoShEpJMWemwo9L4FyUyFRkZIKFSe+kKi59dyPfIS3t26lNsq+\nbbt3szt0cy8Dwnu49vUArq+pwcwoC7/J9jFj/datcYeBnDV9urcpA5vU+/6/vbOTQDDIuUB4w/k6\n4AfAph07En7Hjx93XJRv944RtbWMb2jgyqqqAePRv9Hdzbc7O2kcNSru/unasG0bF/33f6tJXERK\nRrHnpqY1a+Le1/2el0DdtQqNipREVJj4TqLm1re3b6eW2E3Ou0M3p0Do06fv/7t7e6no6eGHVVWD\nj9/Tk3AYyF0JEs2uPXsoB2YBh4YlmxecoxzY2d6e8Dt2JfHEprKsjMlRnvZU5uBpT0dXl5rERaSk\nFHtuSjREsd/zEqi7VqFRkRJPEb9vUgyjUyRqbt0XBozF3hXxst5rEccL/7mzu5u9gH3Clu3q6Rkw\njnui8zfzzs2/7+f+WIAHgBVhMa3lnWb3ZM/xGgx4CTHyO8Xru5yJ34FEQ0Wm2ySuoShFSotyk/9z\nU6HnJVBuKhQqUqIp8taTYm/u3NHWhgM2A6vDbpSb8UYxKQ8EaMN7chWpDejs6mLzjh2sB8JvYetD\nx3hp7dq456+vrqYNuIDBI6i0AT3d3XQDv4uybzewtqUl7vEBRtXX0x7jO7QD+4wcGXcox+27d/OZ\nH/94yL8DNVVVcY+fbt/iRMdPZihKESksyk3+zk0bt2+Pe3y/56Waqqq0W0qUm3JLRUqfEpqAsdib\nO7t7ezFgb2D/sOW78G7MR0yezIeOPJK3o9xw9xk5EgcseuQRxgEHhj3t2uMcBrRFvLAY6YgpU7gb\nuCgQYN+wEUzeCgb5UTDIvuPG8forr8Rs8q8oT/zX8oTDD2fCd74Tczz6U44+mg8eeWTMJ1Lp/g5M\nGjMm7lCR6f4OJTp+If9DRUSiU27yd24qKysjHr/npUljxrBq3bq4x0hEuSm3VKQUeatJPIU+OkWi\n5tb1QF3Ez33O/eAHYx73xoceAuBtYHjY0663Uzh/VSDABLMBs/r2mlEVlhimAIeG7R9tqqx45zjl\n6KNjfgeIPxRl3406ndG5kjl+Ok3iutmLlCblpuj8kpsKNS/Fijnaz/EoN+VOaRcpRfzOSTFL1Nw6\npqGBduASBv6C9+A1OY+qr497/N179sTdv7unJ+75925ooCMQ4PxgEHp7B6zuCAQYVVfXf7zwW23f\nkWqrq/PepJzu6Fz5jl9EJNeKPTeNqK0t6LwEyk2FpjSLlBJuPSkGyXQ1+sMDD3BZWRn7hXWderOn\nh0t6eznh8MPjHr9u2DCqgO8zcCz5V4CvABOTaO4d9+Mf90+8FW5kXR3bd+/mzw8+SEUgQGXY06uK\nYJBAMMghEyfmvUk53dG58h2/iEiuFXtuOubggzn3P/6jYPMSKDcVmtIpUlSYDFLIo1MkajKur6qi\nrryc+rAbWV13N/VJdhkow3sxMbzBuZJ3RkRJdCM7+uCDY6578LnnMDPeYOAwk28AFtYEn4ubZTZH\n59LNXkSGQrkptnznpkLPS6DcVEiKv0hRcTJIsTd31lRV0V1ezrdDL+ENkMT3G15Twx6iN6nvCa1P\nx8i6urhN7iPr6mLsmTnZHp1LRCRVyk2lnZuUlyRSykWKmc0BvgkciTdU90edc/dnOrCMWLZMhUkU\npdDc6ZwjGDH2PEAgyrJIR0yezPgRI/hRRQX7hzXJr+7p4eLubo6YPDmt2I4++GAeiNPkHu9JV6Zk\ne3QukVwqqLwkMSk3xVfsuUl5SSINpSWlFlgO3Azck9lwMiBiKGGJrhhu9rG0d3ZS2dvLD6uro87K\nm8yNrqaigurycqrD9q8G0ntO9Y5cFCKJZHt0LpEc8ndekqQpN8VX7LlJeUnCpVykOOceAh4CsPAO\n9Pmmbl0SYaj9Vv3S5SCfMy/75RqIJMO3eUkkikLOTcpLkkuF/U5KCU3AKLnjhy4H+Z552Q/XQERE\n3pHv+7LykuRa4RYpmuNEsijfNzs/zLyc72sgIiID5fO+rLwkuVZ4RYq6dUmSiqHfaqHPvCwiIgMV\nem5SXpJcyXqRsnjxQpYuXTho+Zw5C5g7d0HyB1JxIklSv1URSWTh4sUsXLp00PIFc+awYO7cPEQk\nxU65SSQ1WS9S5s5NsRgJp8JEhkD9VkUkkQVz56oYkZxSbhJJzVDmSakFDgD6RlCZbGaHAducc2sz\nFpneOZE0FMvNvtC7BYjkQs7ykkiaiiE3KS9JrgylJWUW8ATgQp+fhZbfBpyVdkRqPRFRtwCR1GQ3\nL4mI8pLknLkkZjlNx6JFJD6BChORQfI5Hr0UkfnzNW9INIsWZTf5iRQh5SXJiCTzUn5H91JxIhKT\nbvgiIuInykuSS/krUvTOiYiIiIiIRJHbIkUzxIuIiIiISAK5KVLUrUtERERERJKU/SJF3bpERERE\nRCQFWS9SVJyIiIiIiEgqAvkOQEREREREJJyKFBERERER8RUVKSIiIiIi4isqUkRERERExFdUpIiI\niIiIiK+oSBEREREREV9RkSIiIiIiIr6iIkVERERERHxFRYqIiIiIiPiKihQREREREfEVFSkiIiIi\nIuIrKlJERERERMRXVKSIiIiIiIivqEgRERERERFfUZEiIiIiIiK+oiJFRERERER8ZUhFipl92cxW\nm9keM3vWzN6d6cBERERSodwkIlI8Ui5SzOxTwM+AS4EjgBXA38xsrwzHJiIikhTlJhGR4jKUez5w\ntwAAIABJREFUlpSvAzc65253zq0CzgPagbMyGpmIiEjylJtERIpISkWKmVUARwKP9S1zzjngUeDo\nzIYmIiKSmHKTiEjxSbUlZS+gDNgUsXwTMC4jEYmIiKRGuUlEpMiUZ/sECxcvZuHSpYOWL5gzhwVz\n52b79CIiIoMoN4mI+Jt5LeJJbuw1qbcDH3fO3R+2/FagwTl3SpTdkj+BiIhkmuU7gGxTbhIRKShJ\n5aWUuns557qB54Hj+89iZqGfn07lWCIiIpmg3CQiUnyG0t3r58CtZvY8sAxvRJUa4NYMxiUiIpIK\n5SYRkSKScpHinPtTaNz5y4GxwHLgP5xzLZkOTkREJBnKTSIixSWld1KGSP1+RUTyp+jfSRki5SYR\nkfzI/DspIiIiIiIi2Zb1ImXhwoXZPkXa/B6j4kuP4kuf32NUfJIqv/+Z+D0+8H+Mii89ii99fo/R\n7/GpSMH/MSq+9Ci+9Pk9RsUnqfL7n4nf4wP/x6j40qP40uf3GP0en7p7iYiIiIiIr6hIERERERER\nX1GRIiIiIiIivqIiRUREREREfCXr86SY2QLnnK/fzPF7jIovPYovfX6PUfFJqvz+Z+L3+MD/MSq+\n9Ci+9Pk9Rt/Hl4PJHEVERERERJKm7l4iIiIiIuIrKlJERERERMRXVKSIiIiIiIivqEgRERERERFf\nUZEiIiIiIiK+klaRYmZzzOx+M1tvZkEz+0gS+7zfzJ43sw4ze9XMPptODJmMz8zmhrYL//Sa2d5Z\niu87ZrbMzHaa2SYzu9fMpiaxX06u4VDiy+U1NLPzzGyFmbWGPk+b2QcT7JPL37+U4sv171+U8387\ndM6fJ9guZ9dwKDHm+Hfw0ijneinBPnm7fqVCuSnt+JSb0otPuSmz8fo6N/ktL4XOVxS5Kd2WlFpg\nOfAlIOFYxma2H/AA8BhwGPAL4HdmdkKacWQkvhAHHAiMC332cc5tzk54zAFuAN4DzAMqgIfNbFis\nHXJ8DVOOLyRX13At8C3gXcCRwOPAfWY2LdrGefj9Sym+kFz+/vUzs3cD5wIrEmy3H7m9huHnTirG\nkFxex5XA2LBzvS/Whvm8fiVGuSk9yk3pUW7KEL/nJh/nJSiG3OScy8gHCAIfSbDN1cALEcsWAg9m\nKo4045sL9ALDsx1PjPPvFYrzfT69hsnEl+9ruBU402/XLsn48nLtgDrgFeADwBPAz+Nsm5drmGKM\nObuOwKXAv1LYPu+/g6X2UW7KSIzKTenHqNyUeky+zk1+zUuh8xVFbsr1OymzgUcjlv0NODrHccRj\nwHIz22BmD5vZMTk89wi8SntbnG3yeQ2TiQ/ycA3NLGBmpwE1wDMxNsvbtUsyPsjP79+vgEXOuceT\n2DZf1zCVGCG31/FA87rtNJvZnWY2Mc62hXAPLEWF8Oei3BSbctMQKTelxc95CYogN5Xn+HzjgE0R\nyzYBw82syjnXmeN4Ir0NfAF4DqgCzgGeNLOjnHPLs3liMzPgOuDvzrl4/Qbzcg1TiC+n19DMZuDd\nWKuBXcApzrlVMTbP+bVLMb6c//6FktPhwKwkd8nHNUw1xlxex2eBz+E9TdsH+AGwxMxmOOfaomzv\n93tgqfL7n4tyU/rxKTcNPT7lpvTjy/U1LIrclOsixdecc68Cr4YtetbMpgBfB7L9AtGvgUOA92b5\nPEOVVHx5uIar8PpPNgCnAreb2bFxbra5lnR8ub52ZjYBL7nPc851Z/r4mTCUGHN5HZ1zfwv7caWZ\nLQPeAj4J3JLJc0npUm6KS7lpaJSbhsjveSl0vqLITbnu7rUR7yWecGOBnT54UhXLMuCAbJ7AzH4J\nfAh4v3Pu7QSb5/waphhfNFm7hs65HufcG865fzvnLsZ7ee38GJvn/NqlGF802fz9OxIYA/zLzLrN\nrBuv3+z5ZtYVekIZKdfXcCgxRpP1v8cAzrlWvEQU61yFeA8sBYX456LcpNyUq/iiKeXcVFB5CQo3\nN+W6JeUZ4KSIZScSvx9kvh2O10yXFaGb7MnAXOfcmiR2yek1HEJ80WT1GkYI4DWlRuOH37948UWT\nzWv3KDAzYtmtwMvAVS705lyEXF/DocQYTU5+B82sDi8J3B5jEz/8Dspghfjnotyk3JRJyk3JK6i8\nBAWcm9J56x5vGMXD8C50ELgg9PPE0PorgdvCtt8Pr+/j1cBBeMMvduE1mWVjdINU4zsf+AgwBZiO\n15zXjfeUJhvx/RrYjjec4tiwT3XYNj/O1zUcYnw5u4ahc88B9gVmhP48e4AP+OT3L9X4cvr7FyPm\nASOU5PP3L40Yc/k7+BPg2NCf8THAI3j9eEf79fqVwgflpnTjU25KLz7lpszH7OvclER8uf47XBS5\nKd2LMBfvBtsb8fl9aP0twOMR+xwLPA/sAV4DzsjiL01K8QHfDMXUBrTgjRd9bBbjixZbL/BfYdvk\n7RoOJb5cXkPgd8AboeuwEXiY0E0239duKPHl+vcvRsyPM/BGm9drOJQYc/w7uBBYF7oWa4A/APv7\n+fqVwifVe3+u/1xSjS/X94ah3PtzeQ2HEl+O7wvKTZmP2de5KVF8efg7XBS5yUKBiYiIiIiI+EKu\nX5wXERERERGJS0WKiIiIiIj4iooUERERERHxFRUpIiIiIiLiKypSRERERETEV1SkiIiIiIiIr6hI\nERERERERX1GRIiIiIiIivqIiRUREREREfEVFioiIiIiI+IqKFBERERER8RUVKSIiIiIi4isqUkRE\nRERExFdUpIiIiIiIiK+oSBEREREREV9RkSIiIiIiIr6iIkVERERERHxFRYqIiIiIiPiKihQpOGb2\npJk9ke84RERECpGZfdbMgmb2riS2fdLMHs9FXCLhVKRIVoTdANvNbJ8o6580sxeGeHgHBNOLMHWh\nmINhn04ze8PMbjSzCbmOR0REsicsj0X79JrZUfmOMU0uw9uJZFR5vgOQolcFfBs4P2J5Oje9E9LY\nNx0OWIv3fQyoBA4BvgicaGbTnHMdeYpNREQyzwHfA96Msu713IYiUlpUpEi2LQfOMbMrnXMbM3FA\n51xPJo4zRK3OuYXhC8zsTeAG4L3AY/kISkREsuYh59y/8h2ESKlRdy/JJgf8GK8Y/naijc3sTDN7\nzMw2mVmHmTWZ2XlRtuvvH2tme5tZt5l9L8p2U0PN8l8KW9ZgZteZ2ZrQOV4zs4vMzNL4nptC/+0v\nnsxskpn92sxWhbq8bTGzP5nZvmHb7B+KL7KVCTM7JrTuU2HLGs3s92a2MRT7SjM7M8q+Xw2tazOz\nbWb2TzM7LY3vJyIiUZjZvqF79YVmdo6ZvR66Py8zs1kR2441s1vMbG1omw1m9n9mNiliu5PMbImZ\n7TaznWb2gJkdErHNrWa2y8wmhtbvMrN1ffnOzGaG8uluM3vTzBbE+Aq1oS7LW8ys1cxuM7MRSXzv\nSjO7LJRDO0I59Wozq0zxEorEpJYUybbVwO14rSlXJWhNOQ9YCdyH9w/++cCvzcycc78J266/q5hz\nbrOZLQY+Cfww4ninhY7zvwBmNgxYAuwD/Dde161jgCuBccCFSXyfMjMbHfr/CrzuXj8AXgOeCtvu\n3cBsYCGwDtgP+BLwhJkd4pzrcM6tNrOngNOBX0Sc53RgZ+haYGZ7A/8AeoHrgS3AScDNZlbvnLs+\ntN05oWP9CbgOqAYOBd4D/DGJ7yciIgM1hN33+zjn3Lawn08H6vByiwO+BfzZzCY753pD29wDTMO7\nh78F7I3XfXkSsAbAzM4AbgUeAi4CavC6FC81syOcc2v6zo/3oPmvwGLgm6EYbjCzNuAK4E7gz3i5\n9TYze9o591ZYzAb8EtgOXAochJenJgHHxboYoYd6i/Dy543AKmAm8HXgQOBjsfYVSYlzTh99Mv4B\nPov3D+p3AfsDXcC1YeufAF6I2KcqynH+CrwWsewJ4PGwn88JneuQiO1WAo+E/XwJ3j/8J0ds9+NQ\nfOMTfKcn8F7Yj/ysBPZN4rscFdr+9CixTw1bVg5sBm4OW/Y7vGJnRMQx/wBs6zsfcG/kddVHH330\n0Sf1TyiPRbvnB4H20Db7hn7eDAwP23d+6N7+odDPDaHtLoxzvtrQ/fw3EcvH4BUS/x227JbQ8S8K\nW9YAtOE9nDs1bPnU0Lm/H+W7/QMoC1v+jdBxPxy2LDLnfgboBo6OiPPc0L6z8/1np09xfNTdS7LO\nObcauAM418zGxtmus+//zWx46MnVEmCymdXHOcU9eDfG8K5R0/FaOcJbD04FlgKtZja674P3Hkk5\ncGwSX2c1cDwwD/gg3oAADcBD4U/aIr5LuZmNAt4AduAVbn3+BHTiPQHr80FgNN5TsD4fw3tyVRYR\n+8PAiLBj7gAmRHYzEBGRIXF4LRnzIj4nRWz3R+fczrCfl+K1VEwO/bwH72HY++N0pzoBL5/8MeI+\n7/CKiWitGzf3B+pcK/AK0Oacuzts+at4uWHy4N25yb3T0gPwG0LFVYwYwculLwOvRsT5ROg7x2yF\nEUmFuntJrvwIOAPv3ZSvR9vAzN4LXIbXTaombJXDu3Hvirafc26rmT2G1+Xr0tDi0/Ce9NwbtumB\neE3SLdEOg9f0nkibcy58jpaHQ122nsP7bt8MfZdq4LvA54DxeDfu8O/SF3urmS0CPh0W++nA+r7z\nmNkYvELkXOALCWK/Gq+IWmZmr+MVMX9wzj2dxHcTEZHB/ukSvzi/NvwH59yO0KuOI0M/d5nZt4Cf\nApvM7FngAeB251zfe40H4uWKaPOAObyeAOE6nHNbI5a14rW6R2rtiyXimANGKHPOtZnZ23hdlGM5\nEDiY9HKpSEIqUiQnnPf+xZ14rSlXR643s8nAo3hPZ76Od8PvAv4TuIDEgzz8Efi9mR3qnHsB+ATw\nmBvYZzgAPIL3D/loL8q/mtq38jjn/mVmrQxsifklXnP6tcCzeAnCAf8T5bvcDpxqZrPxuo7ND+0f\nHjd4LSu3xQjjhVAsq8zsIODDeC0yHwO+ZGaXOecuG8r3ExGRhHpjLO/PNc65X5jZ/cBHgf8ALge+\nY2bHOedW4N3rHV53qk1RjhU5smWscyaMJU0B4EW8XB3tmGujLBNJmYoUyaUf4d18vxVl3Xy8eUfm\nO+fW9y00s+OTPPb/4b3A96nQS31T8V4cDNcM1EW0hGRKGd5Lk30+DtzqnLuob4GZVeG1iER6CO9F\n+NOBZcAwBnb1asFrRSpzziWc9dc5twdvsID/NbNyvNaki80bBrorpW8lIiIZE+r+fC1wrZlNAVYA\n/w/4L7wcZUBLMvf6DDC8VpHF/QvMavEGl/lLnP2agUOzlEtF+umdFMkZ59wbeP/4/gLeaFrh+p78\n9P9OmlkDXnepZI7dCvwNr8vXaXjvedwXsdmfgKPN7MTI/c0bmrgsmXNF2fc4vAJledjiXgb//foa\nXjETGXsv3ihgn8L7vi8651aGrQ/ijdDy8dC7NpHn3yvs/0dFHLsHr3XK8EYjExGRHDOzYaEHVeFW\n4z2A6lv+N7wuXd8NPWCKPMZekcsy4NyIc30JL089GGefP+G9+3hO5Aozqzazmij7iKRMLSmSTdGa\nga/AezflILyuTX0exnuH5AEzuxGoBz6P1+QdWdDE8j94RdCXgL9FvMQI8BPgI6Fz3Ao8jzeayqF4\n3aL2wxtZJZ4GM+t7yb0cr1/ueUA7XjeyPg8AZ5jZTuAl4Gi8d0W2xDju7XhFzPvxhp2M9O3Qun+Y\n2W9DxxwFHAl8AOhLXg+b2Ua84ZA34Q0e8GXgAedcW4LvJiIiAxnwITObFmXdU4QNiZ/AVOAxM/sT\n3v27By/v7I33kArn3C4z+yJePviXmf0RryV9El7X57/j5YlMqgyL62BCwx075x6Is88deA8EfxN6\nSPcUXmEzDa+r9YmAJr+UtKlIkWwadPN2zjWb2R1472uEz3fyqpl9HK9L2E+AjcCvga2EjV4S79jA\n/XgjqNQSZU4Q59weMzsW74X2T+AVSzvx3kX5Pt57I4lMwEsgfTFsx3vJ8fLQuzB9voaXhD6NN1fJ\n3/FGhPlbtNhD77U04SWJP0RZv9nMjgrFeQpeItkKNDGwqPlvvG5jX8dr3VmHN19KZNc3ERFJzOEN\n6BLNmXhdpRzRc1L48rV49/bj8bo99+DNL/IJ59z/9e/g3EIzW4/3YOobeK0s6/FGC7slyvFjxRwv\nlvBlX8HLGZfhtbbfhTdqZcxjOuecmZ2Ml2f+C+8dm3a8ESyvZYjvd4pEMueSfQggItlkZv8Ctjrn\nTsh3LCIiIiL5pJYUER8IzWtyON5TKREREZGSppYUkTwKvQg/C7gQ7x2TKRqBS0REREqdRvcSya9T\n8d65KQMWqEARERERUUuKiIiIiIj4jFpSRERERETEV1SkiIiIiIiIr+RidC/1JxMRyZ9ok6qKcpOI\nSL4klZfUkiIiIiIiIr6iIkVERERERHwlpSLFzAJm9kMze8PM2s3sdTO7JFvBiYiIJKLcJCJSfFJ9\nJ+XbwBfwZsV+CW8SulvNbIdz7peZDk5ERCQJyk0iIkUm1SLlaOA+59xDoZ/XmNmngaMyG5aIiEjS\nlJtERIpMqu+kPA0cb2YHApjZYcB7gQczHZiIiEiSlJtERIpMqi0pVwHDgVVm1otX5FzsnPtjxiMT\nERFJjnKTiEiRSbVI+RTwaeA0vH6/hwO/MLMNzrk7Mh2ciIhIEpSbRESKjDmX/HxWZrYGuNI595uw\nZRcDpzvnDom2z8KFC93ChQsHLV+wYAELFixIPWIREUlF0U/mqNwkIlJQkspLqRYpW4DvOuduClv2\nHeCzzrmDY+ymWX1FRPKnFIoU5SYRkcKRVF5KtbvXIuASM1sHNAHvAr4O/C7F44iIiGSKcpOISJFJ\ntSWlFvghcAqwN7AB+APwQ+dcT7R9luxYoqdVIiJ5cuyIY0uhJUW5SUSkQCSbl1JqSXHOtQEXhj4i\nIiJ5p9wkIlJ8Uu3uJSJSEDraO9i1fRfBYDDfoaQsEAgwrH4YNXU1BAKpTmclIiJ+FAwGad/dzp5d\newo2N9WPrKe6pjon51ORIiJFJRgMsvi+xWx8fSPlZeUErPD+kR90QYLBIK7cMfOYmUybNU3FiohI\ngQoGg7z83Mu8+PSLWI8RCAQKNjf19PYw7oBxzD15btbzkooUESkqi+9bTOvaVk6cdyLjJoyjvLzw\nbnN9T9uaX22m6ekmNq/fzHGnHJfvsEREZAgW37eYHW/t4F3T38WUqVMKtpW8p6eHjes2snTpUhbf\ntzjreanwsreISAwd7R1sfH0jJ847kXfNfle+w0nb5KmTGTlqJI89+Rgd7R05a2IXEZHM6GjvYNMb\nmzj+/ccz65hZ+Q4nbY0TGgF4+NGHs56XCq+MExGJYdf2XZSXlTNuwrh8h5IxE/abQLmVs2v7rnyH\nIiIiKdq1fRflVs6E/SbkO5SMGTdhHOVl2c9LKlJEpGgEg0ECFijILl6xBAIBzKwgX7IUESl1wWAQ\nMyvI7l2xlJd773tmOy8VzxUTEREREZGioCJFRERERER8RUWKiIiIiIj4iooUEZEcaG9v56xTzmLa\n6GlMqZ/Ceya/hz/c/Id8hyUiIiXK73lJRYqISA588gOf5MlHnuSYucdw1hfPIlAW4OKvXcz9/3N/\nvkMTEZES5Pe8pCJFRCTL/nrvX1n5wko+ecYnufmem7n4mot59N+PUldfx5WXXJnv8EREpMQUQl5S\nkSIikmX/e9v/YmZc9MOL+pfV1tUy70PzeHv927zS9EoeoxMRkVJTCHmpeCYTEBHJgqYVTexqHTxh\nVX1DPdMPm57UMZpfa2bEiBGM2mvUgOWz58zm3j/ey9NPPM1B0w/KSLwiIlL80s1NhZCXVKSIiMTQ\ntKKJc086ncru7kHruioquOmvdyWVDHa27mR4w/BByyftPwmAdW+tSz9YEREpCZnITYWQl1SkiIjE\nsKt1F5Xd3fy0LMABZWX9y1/v7eUb3d1Rn2JF09PTQ3nF4NttTX0NAB17OjITsIiIFL1M5KZCyEsq\nUkREEjigrIwZFRUDF/YGk96/vLycnu6eQcvbd7UDUD2sOq34RESk9KSTmwohL+nFeRGRLBveMJyd\nrTsHLV+zeg0AE/adkOuQRESkhBVCXlKRIiKSZVMOnMKOHTvYtmXbgOVPP/k0AMccd0w+whIRkRJV\nCHlJRYqISAKv9/aysru7//N6b29K+3/is5/AOcdVl1zVv6y9vZ0n/vYE+zTuk/cRVEREpPCkk5sK\nIS/pnRQRkRjqG+rpqqjgG93dg/r5dlVUUN9Qn9RxTjrlJA6ZeQh333k3WzZtYf8D9ufBex9k9+7d\n/Oi6H2UjdBERKVKZyE2FkJdUpIiIxDD9sOnc9Ne70p4nBeDuJ+7mK6d/hWeWPMPiRxez15i9+NEv\nfsRHTvtIJkMWEZEil6nc5Pe8lFKRYmargX2jrPqVc+6rmQlJRMQ/UilE4qmpqeH39/4+I8eSgZSb\nRKTUZCI3+T0vpdqSMgsoC/t5JvAw8KeMRSQiIpIa5SYRkSKTUpHinNsa/rOZzQeanXNLMxqViIhI\nkpSbRESKz5BH9zKzCuB04ObMhSMiIjJ0yk0iIsUhnRfnTwEagNsyFIsUoU1rN9GyoYUxjWMYO3Fs\nvsMRkeKn3CQJKTeJ+F86RcpZwF+dcxvjbfTo3Y/y2J8fG7T8+I8fz7xT56VxevGztp1t/PaK3/Lc\ns8/RE+yhPFDOrNmzOOfic6gdXpvv8ESkeCk3SUzKTSKFY0hFiplNAuYBH0207bxT5+mGX4J+e8Vv\neXbFs0w6bxIN0xpofbmVZ297Fq6AC66+IN/hiUgRUm6SRJSbRArHUFtSzgI2AQ9mMBYpEpvWbuK5\nZ59j0nmTGDvHa0avnlMNDp678Tk2rd2k5nURyQblJolJuUmksKT84ryZGfA54FbnXDDB5lKCWja0\n0BPsoWFaw4DlDYc00ON6aNnQkqfIRKRYKTdJIspNIoVlKKN7zQMmArdkOBYpEmMax1AeKKf15dYB\ny1tfaqXcyhnTOCZPkYlIEVNukriUm0QKS8rdvZxzjzBw0iyRAcZOHMus2bO8fr7Oe0rV+lIra25f\nw+zZs9WcLiIZp9wkiSg3iRSWdEb3EonpnIvPgSu8fr5r3BrKrZzZs2d7y1OgYSLTp2soIuJRbvIH\nXT9JhooUyYra4bVccPUFQ74RaZjI9OkaiogMpNyUX7p+koohzzgvkoyxE8cy4z0zUn5S0jdMZON5\njcy8YSaN5zXy7Ipn+e0Vv81SpMVH19A/tm/dzhcXfJHjZh7H1BFT2a9mP6753jX5DkukZCk35Yeu\nn7/4PTepJUV8J5PDRJZqk7KG2vSX9WvW89f7/kpdXR3j9hnHmrfW5DskEUmRclN6lJf8x++5SUWK\n+E68YSLXuDW0bGhJeCMr9SblTFxDyZwp06bw5ItPst+U/Xhk0SOc86nU+r+LSP4pN6VHecl//J6b\n1N1LfCcTw0SWepOyhtr0l2HVw9hvyn75DkNE0qDclB7lJf/xe25SS4r4TrrDRKpJWUNtZsOi/13E\nuMZxvPu97853KCKSB8pN6VFeyrye3h5uueEWTj7tZPYet3e+w8k4FSniS+kME6kmZU+mhtoUWL92\nPZde+FNGjKzjb8//HxUVFfkOSUTyQLkpPcpLmXXPnfdw/VV3sPLfK/nFbb/IdzgZpyJFfCmdYSLD\nm5Sr51T3Ly+1JuV0h9qUd/zqql/R1jacPe3t3HnTnZz55TPzHZKI5IFyU3qUlzKnp7eH3//yLva0\nj2HxI8t5fdXrHHDwAfkOK6P0Tor42lCGiexrUl5z2xo2LdlEx5YONi3ZxJrb1zBr9qySuyEOdahN\n8axfu56H7n+asrLP4dw87rjxbrq7u/MdlojkkXJTepSX0nfPnfewZvUuamuvoaNjLDdceUO+Q8o4\ntaRIUVKT8jv0xCo9v7rqV7S3jaS27nP09q7j7fWPqjVFRIZEucmjvJSevlaUYPC9VFa9n97eM1n8\nyFVF15qiIkWKkpqUS3uoy0zpa0UJlH2VQGAEgcAIOvZ4rSmfOfczejdFRFJS6rlJeSkz+lpRqqvP\nA6B62Ons3nULN1x5Q1G9m6LuXlLUSrlJuZSHusyUX131K3bvqiYQ2JuOjr/Q0fEXAmUH8Pb6Du68\n6c58hyciBapUc5PyUvr6WlG6u/elN7iVjo6/0Nn5JM4d0f9uSrFQS4oUtFJ9GpVIqQ91mSmvr1pN\nVXU7cHH/srIysHLHKytfSelY3z//+7TuaGXT25sAePyhx1m/Zj0A3/vJ99hr770yFreI5Jdy02DK\nS5mxc/tOtm/dTvWw7cBX+pdXVgHWy0srXkqpy5efc5OKFClIajKOT0NdZsZdD91O2+62qOuGNwxP\n6Vj3LLyH3bt39/+8qmkVq5pWAXDuheeqSBEpAspNsSkvZcaovUbxxMqH6OrsGrQuEAgUVW5SkSIF\nqa/JeNJ5k2iY1kDry63eBFFXwAVXX5Dv8PJOQ11mRkVFBSNGjsjIsVZuXpmR44iIfyk3xaa8lDk1\nNTXU1NRk5Fh+zk0qUqTgqMk4Mc3sKyKSW8pN8SkvSapUpEjBUZNxcjTUpYhI7ig3Jaa8JKlQkSIF\nR03GySn1oS5FRHJJuSkx5SVJhYoU8bVoN7JUmoyL4UaY7ncYO3FswX53ERE/KvXcpLwkuaAiRXwp\n0QgpiZqMi2GElWL4DiIixaTUc1Ohxy+FJeUixcwagauBk4Aa4DXgTOfcvzIcm5SwRCOkJGoyLoYR\nVorhO4jkinKT5EKp56ZCj18KS0pFipmNAJ4CHgP+A9gCHAhsz3xoUqpSGSElWpNxMYywUgzfQSRX\nlJskF0o9NxV6/FJ4Ailu/21gjXPu8865551zbznnHnXOrc5GcFKa4o2Q0uN6aNnQktX9w21au4mV\n/1jJprWbhrR+qDL5HURKgHKTZF2p5yblJcm1VLt7zQceMrM/AXOB9cCvnXO/y3hkUrJCojQ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ixRbpJBlJtEsi/ZvDSUlpSVwPFA3wl6hnAMyZBELTEtLWvYsmUde+01gWW8GXWbdIqXdJq8x180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\nto8FjZfwq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VHbKitrtOqu5JSmhIk4l3KTSPFQkSKjnOkz34DffwyX6yb8/rvp75+S0Kq6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112a246d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mlxtend.plotting import plot_decision_regions\n",
    "import matplotlib.gridspec as gridspec\n",
    "import itertools\n",
    "\n",
    "gs = gridspec.GridSpec(2, 2)\n",
    "\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "\n",
    "labels = ['Logistic Regression', 'Random Forest', 'Naive Bayes', 'Ensemble']\n",
    "for clf, lab, grd in zip([clf1, clf2, clf3, eclf],\n",
    "                         labels,\n",
    "                         itertools.product([0, 1], repeat=2)):\n",
    "\n",
    "    clf.fit(X, y)\n",
    "    ax = plt.subplot(gs[grd[0], grd[1]])\n",
    "    fig = plot_decision_regions(X=X, y=y, clf=clf)\n",
    "    plt.title(lab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2 - Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X, y = iris.data[:, 1:3], iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.953 +/- 0.01 {'logisticregression__C': 1.0, 'randomforestclassifier__n_estimators': 20}\n",
      "0.960 +/- 0.01 {'logisticregression__C': 1.0, 'randomforestclassifier__n_estimators': 200}\n",
      "0.960 +/- 0.01 {'logisticregression__C': 100.0, 'randomforestclassifier__n_estimators': 20}\n",
      "0.953 +/- 0.02 {'logisticregression__C': 100.0, 'randomforestclassifier__n_estimators': 200}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from mlxtend.classifier import EnsembleVoteClassifier\n",
    "\n",
    "clf1 = LogisticRegression(random_state=1)\n",
    "clf2 = RandomForestClassifier(random_state=1)\n",
    "clf3 = GaussianNB()\n",
    "eclf = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], voting='soft')\n",
    "\n",
    "params = {'logisticregression__C': [1.0, 100.0],\n",
    "          'randomforestclassifier__n_estimators': [20, 200],}\n",
    "\n",
    "grid = GridSearchCV(estimator=eclf, param_grid=params, cv=5)\n",
    "grid.fit(iris.data, iris.target)\n",
    "    \n",
    "cv_keys = ('mean_test_score', 'std_test_score', 'params')\n",
    "\n",
    "for r, _ in enumerate(grid.cv_results_['mean_test_score']):\n",
    "    print(\"%0.3f +/- %0.2f %r\"\n",
    "          % (grid.cv_results_[cv_keys[0]][r],\n",
    "             grid.cv_results_[cv_keys[1]][r] / 2.0,\n",
    "             grid.cv_results_[cv_keys[2]][r]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: If the `EnsembleClassifier` is initialized with multiple similar estimator objects, the estimator names are modified with consecutive integer indices, for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clf1 = LogisticRegression(random_state=1)\n",
    "clf2 = RandomForestClassifier(random_state=1)\n",
    "eclf = EnsembleVoteClassifier(clfs=[clf1, clf1, clf2], \n",
    "                              voting='soft')\n",
    "\n",
    "params = {'logisticregression-1__C': [1.0, 100.0],\n",
    "          'logisticregression-2__C': [1.0, 100.0],\n",
    "          'randomforestclassifier__n_estimators': [20, 200],}\n",
    "\n",
    "grid = GridSearchCV(estimator=eclf, param_grid=params, cv=5)\n",
    "grid = grid.fit(iris.data, iris.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 3 - Majority voting with classifiers trained on different feature subsets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature selection algorithms implemented in scikit-learn as well as the `SequentialFeatureSelector` implement a `transform` method that passes the reduced feature subset to the next item in a `Pipeline`.\n",
    "\n",
    "For example, the method\n",
    "\n",
    "    def transform(self, X):\n",
    "        return X[:, self.k_feature_idx_]\n",
    "        \n",
    "returns the best feature columns, `k_feature_idx_`, given a dataset X."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, we simply need to construct a `Pipeline` consisting of the feature selector and the classifier in order to select different feature subsets for different algorithms. During `fitting`, the optimal feature subsets are automatically determined via the `GridSearchCV` object, and by calling `predict`, the fitted feature selector in the pipeline only passes these columns along, which resulted in the best performance for the respective classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.953 +/- 0.01 {'pipeline__sfs__k_features': 1, 'pipeline__logreg__C': 1.0, 'randomforestclassifier__n_estimators': 20}\n",
      "0.947 +/- 0.02 {'pipeline__sfs__k_features': 1, 'pipeline__logreg__C': 1.0, 'randomforestclassifier__n_estimators': 200}\n",
      "0.953 +/- 0.01 {'pipeline__sfs__k_features': 2, 'pipeline__logreg__C': 1.0, 'randomforestclassifier__n_estimators': 20}\n",
      "0.947 +/- 0.02 {'pipeline__sfs__k_features': 2, 'pipeline__logreg__C': 1.0, 'randomforestclassifier__n_estimators': 200}\n",
      "0.953 +/- 0.01 {'pipeline__sfs__k_features': 3, 'pipeline__logreg__C': 1.0, 'randomforestclassifier__n_estimators': 20}\n",
      "0.953 +/- 0.02 {'pipeline__sfs__k_features': 3, 'pipeline__logreg__C': 1.0, 'randomforestclassifier__n_estimators': 200}\n",
      "0.947 +/- 0.02 {'pipeline__sfs__k_features': 1, 'pipeline__logreg__C': 100.0, 'randomforestclassifier__n_estimators': 20}\n",
      "0.953 +/- 0.02 {'pipeline__sfs__k_features': 1, 'pipeline__logreg__C': 100.0, 'randomforestclassifier__n_estimators': 200}\n",
      "0.947 +/- 0.02 {'pipeline__sfs__k_features': 2, 'pipeline__logreg__C': 100.0, 'randomforestclassifier__n_estimators': 20}\n",
      "0.947 +/- 0.02 {'pipeline__sfs__k_features': 2, 'pipeline__logreg__C': 100.0, 'randomforestclassifier__n_estimators': 200}\n",
      "0.960 +/- 0.01 {'pipeline__sfs__k_features': 3, 'pipeline__logreg__C': 100.0, 'randomforestclassifier__n_estimators': 20}\n",
      "0.953 +/- 0.02 {'pipeline__sfs__k_features': 3, 'pipeline__logreg__C': 100.0, 'randomforestclassifier__n_estimators': 200}\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X, y = iris.data[:, :], iris.target\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from mlxtend.classifier import EnsembleVoteClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from mlxtend.feature_selection import SequentialFeatureSelector\n",
    "\n",
    "clf1 = LogisticRegression(random_state=1)\n",
    "clf2 = RandomForestClassifier(random_state=1)\n",
    "clf3 = GaussianNB()\n",
    "\n",
    "# Creating a feature-selection-classifier pipeline\n",
    "\n",
    "sfs1 = SequentialFeatureSelector(clf1, \n",
    "                                 k_features=4,\n",
    "                                 forward=True, \n",
    "                                 floating=False, \n",
    "                                 scoring='accuracy',\n",
    "                                 verbose=0,\n",
    "                                 cv=0)\n",
    "\n",
    "clf1_pipe = Pipeline([('sfs', sfs1),\n",
    "                      ('logreg', clf1)])\n",
    "\n",
    "eclf = EnsembleVoteClassifier(clfs=[clf1_pipe, clf2, clf3], \n",
    "                              voting='soft')\n",
    "\n",
    "\n",
    "params = {'pipeline__sfs__k_features': [1, 2, 3],\n",
    "          'pipeline__logreg__C': [1.0, 100.0],\n",
    "          'randomforestclassifier__n_estimators': [20, 200]}\n",
    "\n",
    "grid = GridSearchCV(estimator=eclf, param_grid=params, cv=5)\n",
    "grid.fit(iris.data, iris.target)\n",
    "\n",
    "\n",
    "\n",
    "cv_keys = ('mean_test_score', 'std_test_score', 'params')\n",
    "\n",
    "for r, _ in enumerate(grid.cv_results_['mean_test_score']):\n",
    "    print(\"%0.3f +/- %0.2f %r\"\n",
    "          % (grid.cv_results_[cv_keys[0]][r],\n",
    "             grid.cv_results_[cv_keys[1]][r] / 2.0,\n",
    "             grid.cv_results_[cv_keys[2]][r]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best parameters determined via GridSearch are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'pipeline__logreg__C': 100.0,\n",
       " 'pipeline__sfs__k_features': 3,\n",
       " 'randomforestclassifier__n_estimators': 20}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we assign these parameters to the ensemble voting classifier, fit the models on the complete training set, and perform a prediction on 3 samples from the Iris dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eclf = eclf.set_params(**grid.best_params_)\n",
    "eclf.fit(X, y).predict(X[[1, 51, 149]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Manual Approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can select different columns \"manually\" using the `ColumnSelector` object. In this example, we select only the first (sepal length) and third (petal length) column for the logistic regression classifier (`clf1`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mlxtend.feature_selection import ColumnSelector\n",
    "\n",
    "\n",
    "col_sel = ColumnSelector(cols=[0, 2])\n",
    "\n",
    "clf1_pipe = Pipeline([('sel', col_sel),\n",
    "                      ('logreg', clf1)])\n",
    "\n",
    "eclf = EnsembleVoteClassifier(clfs=[clf1_pipe, clf2, clf3],\n",
    "                              voting='soft')\n",
    "eclf.fit(X, y).predict(X[[1, 51, 149]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Furthermore, we can fit the `SequentialFeatureSelector` separately, outside the grid search hyperparameter optimization pipeline. Here, we determine the best features first, and then we construct a pipeline using these \"fixed,\" best features as seed for the `ColumnSelector`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Features: 2/2"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best features (0, 2)\n"
     ]
    }
   ],
   "source": [
    "sfs1 = SequentialFeatureSelector(clf1, \n",
    "                                 k_features=2,\n",
    "                                 forward=True, \n",
    "                                 floating=False, \n",
    "                                 scoring='accuracy',\n",
    "                                 verbose=1,\n",
    "                                 cv=0)\n",
    "\n",
    "sfs1.fit(X, y)\n",
    "\n",
    "print('Best features', sfs1.k_feature_idx_)\n",
    "\n",
    "col_sel = ColumnSelector(cols=sfs1.k_feature_idx_)\n",
    "\n",
    "clf1_pipe = Pipeline([('sel', col_sel),\n",
    "                      ('logreg', clf1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eclf = EnsembleVoteClassifier(clfs=[clf1_pipe, clf2, clf3], \n",
    "                              voting='soft')\n",
    "eclf.fit(X, y).predict(X[[1, 51, 149]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 5 - Using Pre-fitted Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X, y = iris.data[:, 1:3], iris.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assume that we previously fitted our classifiers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import numpy as np\n",
    "\n",
    "clf1 = LogisticRegression(random_state=1)\n",
    "clf2 = RandomForestClassifier(random_state=1)\n",
    "clf3 = GaussianNB()\n",
    "\n",
    "for clf in (clf1, clf2, clf3):\n",
    "    clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By setting `refit=False`, the `EnsembleVoteClassifier` will not re-fit these classifers to save computational time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.973333333333\n"
     ]
    }
   ],
   "source": [
    "from mlxtend.classifier import EnsembleVoteClassifier\n",
    "import copy\n",
    "eclf = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], weights=[1,1,1], refit=False)\n",
    "\n",
    "labels = ['Logistic Regression', 'Random Forest', 'Naive Bayes', 'Ensemble']\n",
    "\n",
    "eclf.fit(X, y)\n",
    "\n",
    "print('accuracy:', np.mean(y == eclf.predict(X)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, please note that `refit=False` is incompatible to any form of cross-validation that is done in e.g., `model_selection.cross_val_score` or `model_selection.GridSearchCV`, etc., since it would require the classifiers to be refit to the training folds. Thus, only use `refit=False` if you want to make a prediction directly without cross-validation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## EnsembleVoteClassifier\n",
      "\n",
      "*EnsembleVoteClassifier(clfs, voting='hard', weights=None, verbose=0, refit=True)*\n",
      "\n",
      "Soft Voting/Majority Rule classifier for scikit-learn estimators.\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `clfs` : array-like, shape = [n_classifiers]\n",
      "\n",
      "    A list of classifiers.\n",
      "    Invoking the `fit` method on the `VotingClassifier` will fit clones\n",
      "    of those original classifiers that will\n",
      "    be stored in the class attribute\n",
      "    `self.clfs_` if `refit=True` (default).\n",
      "\n",
      "- `voting` : str, {'hard', 'soft'} (default='hard')\n",
      "\n",
      "    If 'hard', uses predicted class labels for majority rule voting.\n",
      "    Else if 'soft', predicts the class label based on the argmax of\n",
      "    the sums of the predicted probalities, which is recommended for\n",
      "    an ensemble of well-calibrated classifiers.\n",
      "\n",
      "- `weights` : array-like, shape = [n_classifiers], optional (default=`None`)\n",
      "\n",
      "    Sequence of weights (`float` or `int`) to weight the occurances of\n",
      "    predicted class labels (`hard` voting) or class probabilities\n",
      "    before averaging (`soft` voting). Uses uniform weights if `None`.\n",
      "\n",
      "- `verbose` : int, optional (default=0)\n",
      "\n",
      "    Controls the verbosity of the building process.\n",
      "    - `verbose=0` (default): Prints nothing\n",
      "    - `verbose=1`: Prints the number & name of the clf being fitted\n",
      "    - `verbose=2`: Prints info about the parameters of the clf being fitted\n",
      "    - `verbose>2`: Changes `verbose` param of the underlying clf to\n",
      "    self.verbose - 2\n",
      "\n",
      "- `refit` : bool (default: True)\n",
      "\n",
      "    Refits classifiers in `clfs` if True; uses references to the `clfs`,\n",
      "    otherwise (assumes that the classifiers were already fit).\n",
      "    Note: refit=False is incompatible to mist scikit-learn wrappers!\n",
      "    For instance, if any form of cross-validation is performed\n",
      "    this would require the re-fitting classifiers to training folds, which\n",
      "    would raise a NotFitterError if refit=False.\n",
      "    (New in mlxtend v0.6.)\n",
      "\n",
      "**Attributes**\n",
      "\n",
      "- `classes_` : array-like, shape = [n_predictions]\n",
      "\n",
      "\n",
      "- `clf` : array-like, shape = [n_predictions]\n",
      "\n",
      "    The unmodified input classifiers\n",
      "\n",
      "- `clf_` : array-like, shape = [n_predictions]\n",
      "\n",
      "    Fitted clones of the input classifiers\n",
      "\n",
      "**Examples**\n",
      "\n",
      "    >>> import numpy as np\n",
      "    >>> from sklearn.linear_model import LogisticRegression\n",
      "    >>> from sklearn.naive_bayes import GaussianNB\n",
      "    >>> from sklearn.ensemble import RandomForestClassifier\n",
      "    >>> from mlxtend.sklearn import EnsembleVoteClassifier\n",
      "    >>> clf1 = LogisticRegression(random_seed=1)\n",
      "    >>> clf2 = RandomForestClassifier(random_seed=1)\n",
      "    >>> clf3 = GaussianNB()\n",
      "    >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])\n",
      "    >>> y = np.array([1, 1, 1, 2, 2, 2])\n",
      "    >>> eclf1 = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3],\n",
      "    ... voting='hard', verbose=1)\n",
      "    >>> eclf1 = eclf1.fit(X, y)\n",
      "    >>> print(eclf1.predict(X))\n",
      "    [1 1 1 2 2 2]\n",
      "    >>> eclf2 = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], voting='soft')\n",
      "    >>> eclf2 = eclf2.fit(X, y)\n",
      "    >>> print(eclf2.predict(X))\n",
      "    [1 1 1 2 2 2]\n",
      "    >>> eclf3 = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3],\n",
      "    ...                          voting='soft', weights=[2,1,1])\n",
      "    >>> eclf3 = eclf3.fit(X, y)\n",
      "    >>> print(eclf3.predict(X))\n",
      "    [1 1 1 2 2 2]\n",
      "    >>>\n",
      "\n",
      "### Methods\n",
      "\n",
      "<hr>\n",
      "\n",
      "*fit(X, y)*\n",
      "\n",
      "Learn weight coefficients from training data for each classifier.\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `X` : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
      "\n",
      "    Training vectors, where n_samples is the number of samples and\n",
      "    n_features is the number of features.\n",
      "\n",
      "\n",
      "- `y` : array-like, shape = [n_samples]\n",
      "\n",
      "    Target values.\n",
      "\n",
      "**Returns**\n",
      "\n",
      "- `self` : object\n",
      "\n",
      "\n",
      "<hr>\n",
      "\n",
      "*fit_transform(X, y=None, **fit_params)*\n",
      "\n",
      "Fit to data, then transform it.\n",
      "\n",
      "Fits transformer to X and y with optional parameters fit_params\n",
      "and returns a transformed version of X.\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `X` : numpy array of shape [n_samples, n_features]\n",
      "\n",
      "    Training set.\n",
      "\n",
      "\n",
      "- `y` : numpy array of shape [n_samples]\n",
      "\n",
      "    Target values.\n",
      "\n",
      "**Returns**\n",
      "\n",
      "- `X_new` : numpy array of shape [n_samples, n_features_new]\n",
      "\n",
      "    Transformed array.\n",
      "\n",
      "<hr>\n",
      "\n",
      "*get_params(deep=True)*\n",
      "\n",
      "Return estimator parameter names for GridSearch support.\n",
      "\n",
      "<hr>\n",
      "\n",
      "*predict(X)*\n",
      "\n",
      "Predict class labels for X.\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `X` : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
      "\n",
      "    Training vectors, where n_samples is the number of samples and\n",
      "    n_features is the number of features.\n",
      "\n",
      "**Returns**\n",
      "\n",
      "- `maj` : array-like, shape = [n_samples]\n",
      "\n",
      "    Predicted class labels.\n",
      "\n",
      "<hr>\n",
      "\n",
      "*predict_proba(X)*\n",
      "\n",
      "Predict class probabilities for X.\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `X` : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
      "\n",
      "    Training vectors, where n_samples is the number of samples and\n",
      "    n_features is the number of features.\n",
      "\n",
      "**Returns**\n",
      "\n",
      "- `avg` : array-like, shape = [n_samples, n_classes]\n",
      "\n",
      "    Weighted average probability for each class per sample.\n",
      "\n",
      "<hr>\n",
      "\n",
      "*score(X, y, sample_weight=None)*\n",
      "\n",
      "Returns the mean accuracy on the given test data and labels.\n",
      "\n",
      "In multi-label classification, this is the subset accuracy\n",
      "which is a harsh metric since you require for each sample that\n",
      "each label set be correctly predicted.\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `X` : array-like, shape = (n_samples, n_features)\n",
      "\n",
      "    Test samples.\n",
      "\n",
      "\n",
      "- `y` : array-like, shape = (n_samples) or (n_samples, n_outputs)\n",
      "\n",
      "    True labels for X.\n",
      "\n",
      "\n",
      "- `sample_weight` : array-like, shape = [n_samples], optional\n",
      "\n",
      "    Sample weights.\n",
      "\n",
      "**Returns**\n",
      "\n",
      "- `score` : float\n",
      "\n",
      "    Mean accuracy of self.predict(X) wrt. y.\n",
      "\n",
      "<hr>\n",
      "\n",
      "*set_params(**params)*\n",
      "\n",
      "Set the parameters of this estimator.\n",
      "\n",
      "The method works on simple estimators as well as on nested objects\n",
      "(such as pipelines). The latter have parameters of the form\n",
      "``<component>__<parameter>`` so that it's possible to update each\n",
      "component of a nested object.\n",
      "\n",
      "**Returns**\n",
      "\n",
      "self\n",
      "\n",
      "<hr>\n",
      "\n",
      "*transform(X)*\n",
      "\n",
      "Return class labels or probabilities for X for each estimator.\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `X` : {array-like, sparse matrix}, shape = [n_samples, n_features]\n",
      "\n",
      "    Training vectors, where n_samples is the number of samples and\n",
      "    n_features is the number of features.\n",
      "\n",
      "**Returns**\n",
      "\n",
      "- `If `voting='soft'`` : array-like = [n_classifiers, n_samples, n_classes]\n",
      "\n",
      "    Class probabilties calculated by each classifier.\n",
      "\n",
      "- `If `voting='hard'`` : array-like = [n_classifiers, n_samples]\n",
      "\n",
      "    Class labels predicted by each classifier.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('../../api_modules/mlxtend.classifier/EnsembleVoteClassifier.md', 'r') as f:\n",
    "    print(f.read())"
   ]
  }
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